Light field salient object detection (SOD) is an essential research topic in computer vision, but robust saliency detection in complex scenes is still very challenging. We propose a new method for accurate and robust light field SOD via convolutional neural networks containing feature enhancement modules. First, the light field dataset is extended by geometric transformations such as stretching, cropping, flipping, and rotating. Next, two feature enhancement modules are designed to extract features from RGB images and depth maps, respectively. The obtained feature maps are fed into a two-stream network to train the light field SOD. We propose a mutual attention approach in this process, extracting and fusing features from RGB images and depth maps. Therefore, our network can generate an accurate saliency map from the input light field images after training. The obtained saliency map can provide reliable a priori information for tasks such as semantic segmentation, target recognition, and visual tracking. Experimental results show that the proposed method achieves excellent detection performance in public benchmark datasets and outperforms the state-of-the-art methods. We also verify the generalization and stability of the method in real-world experiments.

An event camera (EC) is a bioinspired vision sensor with the advantages of a high temporal resolution, high dynamic range, and low latency. Due to the inherent sparsity of space target imaging data, EC becomes an ideal imaging sensor for space target detection. In this work, we conduct detection of small space targets using a CeleX-V camera with a megapixel resolution. We propose a target detection method based on field segmentation, utilizing the event output characteristics of an EC. This method enables real-time monitoring of the spatial positions of space targets within the camera’s field of view. The effectiveness of this approach is validated through experiments involving real-world observations of space targets. Using the proposed method, real-time observation of space targets with a megapixel resolution EC becomes feasible, demonstrating substantial practical potential in the field of space target detection.

Yolo series models are extensive within the domain of object detection. Aiming at the challenge of small object detection, we analyze the limitations of existing detection models and propose a Vis-YOLO object detection algorithm based on YOLOv8s. First, the down-sampling times are reduced to retain more features, and the detection head is replaced to adapt to the small object. Then, deformable convolutional networks are used to improve the C2f module, improving its feature extraction ability. Finally, the separation and enhancement attention module is introduced to the model to give more weight to the useful information. Experiments show that the improved Vis-YOLO model outperforms the YOLOv8s model on the visdrone-2019 dataset. The precision improved by 5.4%, the recall by 6.3%, and the mAP50 by 6.8%. Moreover, Vis-YOLO models are smaller and suitable for mobile deployment. This research provides a new method and idea for small object detection, which has excellent potential application value.

X-ray imaging is essential for security inspection; nevertheless, the penetrability of X-rays can cause objects within a package to overlap in X-ray images, leading to reduced accuracy in manual inspection and increased difficulty in auxiliary inspection techniques. Existing methods mainly focus on object detection to enhance the detection ability of models for overlapping regions by augmenting image features, including color, texture, and semantic information. However, these approaches do not address the underlying issue of overlap. We propose a novel method for separating overlapping objects in X-ray images from the perspective of image inpainting. Specifically, the separation method involves using a vision transformer (ViT) to construct a generative adversarial network (GAN) model that requires a hand-created trimap as input. In addition, we present an end-to-end approach that integrates Mask Region-based Convolutional Neural Network with the separation network to achieve fully automated separation of overlapping objects. Given the lack of datasets appropriate for training separation networks, we created MaskXray, a collection of X-ray images that includes overlapping images, trimap, and individual object images. Our proposed generative separation network was tested in experiments and demonstrated its ability to accurately separate overlapping objects in X-ray images. These results demonstrate the efficacy of our approach and make significant contributions to the field of X-ray image analysis.

Local backlight dimming (LBD) is a promising technique for improving the contrast ratio and saving power consumption for liquid crystal displays. LBD consists of two crucial parts, i.e., backlight luminance determination, which locally controls the luminance of each sub-block of the backlight unit (BLU), and pixel compensation, which compensates for the reduction of pixel intensity, to achieve pleasing visual quality. However, the limitations of the current deep learning–based pixel compensation methods come from two aspects. First, it is difficult for a vanilla image-to-image translation strategy to learn the mapping relations between the input image and the compensated image, especially without considering the dimming levels. Second, the extensive model parameters make these methods hard to be deployed in industrial applications. To address these issues, we reformulate pixel compensation as an input-specific curve estimation task. Specifically, a deep lightweight network, namely, the curve estimation network (CENet), takes both the original input image and the dimmed BLUs as input, to estimate a set of high-order curves. Then, these curves are applied iteratively to adjust the intensity of each pixel to obtain a compensated image. Given the determined BLUs, the proposed CENet can be trained in an end-to-end manner. This implies that our proposed method is compatible with any backlight dimming strategies. Extensive evaluation results on the DIVerse 2K (DIV2K) dataset highlight the superiority of the proposed CENet-integrated local dimming framework, in terms of model size and visual quality of displayed content.

Video anomaly detection (VAD) is essential for monitoring systems. The prediction-based methods identify anomalies by comparing differences between the predicted and real frames. We propose an unsupervised VAD method based on frame memory bank (FMB) and decoupled asymmetric convolution (DAConv), which addresses three problems encountered with auto-encoders (AE) in VAD: (1) how to mitigate the noise resulting from jittering between frames, which is ignored; (2) how to alleviate the insufficient utilization of temporal information by traditional two-dimensional (2D) convolution and the burden for more computing resources in three-dimensional (3D) convolution; and (3) how to make full use of normal data to improve the reliability of anomaly discrimination. Specifically, we initially design a separate network to calibrate video frames within the dataset. Second, we design DAConv to extract features from the video, addressing the absence of temporal dimension information in 2D convolutions and the high computational complexity of 3D convolutions. Concurrently, the interval-frame mechanism mitigates the problem of information redundancy caused by data reuse. Finally, we embed an FMB to store features of normal events, amplifying the contrast between normal and abnormal frames. We conduct extensive experiments on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets, achieving AUC values of 98.7%, 90.4%, and 74.8%, respectively, which fully demonstrates the rationality and effectiveness of the proposed method.

Recent advancements in transformer architectures have significantly enhanced image-denoising algorithms, surpassing the limitations of traditional convolutional neural networks by more effectively modeling global interactions through advanced attention mechanisms. In the domain of single-image denoising, noise manifests across various scales. This is especially evident in intricate scenarios, necessitating the comprehensive capture of multi-scale information inherent in the image. To solve transformer’s lack of multi-scale image analysis capability, a discrete wavelet and transformer based network (DTSIDNet) is proposed. The network adeptly resolves the inherent limitations of the transformer architecture by integrating the discrete wavelet transform. DTSIDNet independently manages image data at various scales, which greatly improves both adaptability and efficiency in environments with complex noise. The network’s self-attention mechanism dynamically shifts focus among different scales, efficiently capturing an extensive array of image features, thereby significantly enhancing the denoising outcome. This approach not only boosts the precision of denoising but also enhances the utilization of computational resources, striking an optimal balance between efficiency and high performance. Experiments on real-world and synthetic noise scenarios show that DTSIDNet delivers high image quality with low computational demands, indicating its superior performance in denoising tasks with efficient resource use.

Crowd counting has gained widespread attention in the fields of public safety management, video surveillance, and emergency response. Currently, background interference and scale variation of the head are still intractable problems. We propose an attention-injective scale aggregation network (ASANet) to cope with the above problems. ASANet consists of three parts: shallow feature attention network (SFAN), multi-level feature aggregation (MLFA) module, and density map generation (DMG) network. SFAN effectively overcomes the noise impact of a cluttered background by cross-injecting the attention module in the truncated VGG16 structure. To fully utilize the multi-scale crowd information embedded in the feature layers at different positions, we densely connect the multi-layer feature maps in the MLFA module to solve the scale variation problem. In addition, to capture large-scale head information, the DMG network introduces successive dilated convolutional layers to further expand the receptive field of the model, thus improving the accuracy of crowd counting. We conduct extensive experiments on five public datasets (ShanghaiTech Part_A, ShanghaiTech Part_B, UCF_QNRF, UCF_CC_50, JHU-Crowd++), and the results show that ASANet outperforms most of the existing methods in terms of counting and at the same time demonstrates satisfactory superiority in dealing with background noise in different scenes.

The current smart cooler’s commodity identification system first locates the item being purchased, followed by feature extraction and matching. However, this method often suffers from inaccuracies due to the presence of background in the detection frame, leading to missed detections and misidentifications. To address these issues, we propose an end-to-end You Only Look Once (YOLO) for detection and segmentation algorithm. In the backbone network, we combine deformable convolution with a channel-to-pixel (C2f) module to enhance the model feature extraction capability. In the neck network, we introduce an optimized feature fusion structure, which is based on the weighted bi-directional feature pyramid. To further enhance the model’s understanding of both global and local context, a triple feature encoding module is employed, seamlessly fusing multi-scale features for improved performance. The convolutional block attention module is connected to the improved C2f module to enhance the network’s attention to the commodity image channel and spatial information. A supplementary segmentation branch is incorporated into the head of the network, allowing it to not only detect targets within the image but also generate precise segmentation masks for each detected object, thereby enhancing its multi-task capabilities. Compared with YOLOv8, for box and mask, the precision increases by 3% and 4.7%, recall increases by 2.8% and 4.7%, and mean average precision (mAP) increases by 4.9% and 14%. The frames per second is 119, which meets the demand for real-time detection. The results of comparative and ablation studies confirm the high accuracy and performance of the proposed algorithm, solidifying its foundation for fine-grained commodity identification.

With the substantial surge in available internet video data, the intricate task of video summarization has consistently attracted the computer vision research community to summarize the videos meaningfully. Many recent summarization techniques leverage bidirectional long short-term memory for its proficiency in modeling temporal dependencies. However, its effectiveness is limited to short-duration video clips, typically up to 90 to 100 frames. To address this constraint, the proposed approach incorporates global and local multi-head attention, effectively capturing temporal dependencies at both global and local levels. This enhancement enables parallel computation, thereby improving overall performance for longer videos. This work considers video summarization as a supervised learning task and introduces a deep summarization architecture called multi-head attention with reinforcement learning (MHA-RL). The architecture comprises a pretrained convolutional neural network for extracting features from video frames, along with global and local multi-head attention mechanisms for predicting frame importance scores. Additionally, the network integrates an RL-based regressor network to consider the diversity and representativeness of the generated video summary. Extensive experimentation is conducted on benchmark datasets, such as TVSum and SumMe. The proposed method exhibits improved performance compared to the majority of state-of-the-art summarization techniques, as indicated by both qualitative and quantitative results.

Image inpainting techniques based on deep learning have shown significant improvements by introducing structure priors, but still generate structure distortion or textures fuzzy for large missing areas. This is mainly because series networks have inherent disadvantages: employing unreasonable structural priors will inevitably lead to severe mistakes in the second stage of cascade inpainting framework. To address this issue, an appearance flow-based structure prior (AFSP) guided image inpainting is proposed. In the first stage, a structure generator regards edge-preserved smooth images as global structures of images and then appearance flow warps small-scale features in input and flows to corrupted regions. In the second stage, a texture generator using contextual attention is designed to yield image high-frequency details after obtaining reasonable structure priors. Compared with state-of-the-art approaches, the proposed AFSP achieved visually more realistic results. Compared on the Places2 dataset, the most challenging with 1.8 million high-resolution images of 365 complex scenes, shows that AFSP was 1.1731 dB higher than the average peak signal-to-noise ratio for EdgeConnect.

Multi-view clustering has garnered significant attention due to its ability to explore shared information from multiple views. Applications of multi-view clustering include image and video analysis, bioinformatics, and social network analysis, in which integrating diverse data sources enhances data understanding and insights. However, existing multi-view models suffer from the following limitations: (1) directly extracting latent representations from raw data using encoders is susceptible to interference from noise and other factors and (2) complementary information among different views is often overlooked, resulting in the loss of crucial unique information from each view. Therefore, we propose a distinctive double-level deep multi-view collaborative learning approach. Our method further processes the latent representations learned by the encoder through multiple layers of perceptrons to obtain richer semantic information. In addition, we introduce dual-path guidance at both the feature and label levels to facilitate the learning of complementary information across different views. Furthermore, we introduce pre-clustering methods to guide mutual learning among different views through pseudo-labels. Experimental results on four image datasets (Caltech-5V, STL10, Cifar10, Cifar100) demonstrate that our method achieves state-of-the-art clustering performance, evaluated using standard metrics, including accuracy, normalized mutual information, and purity. We compare our proposed method with existing clustering algorithms to validate its effectiveness.

Camera localization is a technique for obtaining the camera’s six degrees of freedom using the camera as a sensor input. It is widely used in augmented reality, autonomous driving, virtual reality, etc. In recent years, with the development of deep-learning technology, absolute pose regression has gained wide attention as an end-to-end learning-based localization method. The typical architecture is constructed by a convolutional backbone and a multilayer perception (MLP) regression header composed of multiple fully connected layers. Typically, the two-dimensional feature maps extracted by the convolutional backbone have to be flattened and passed into the fully connected layer for pose regression. However, this operation will result in the loss of crucial pixel position information carried by the two-dimensional feature map and adversely affect the accuracy of the pose estimation. We propose a parallel structure, termed SMLoc, using a spatial MLP to aggregate position and orientation information from feature maps, respectively, reducing the loss of pixel position information. Our approach achieves superior performance on common indoor and outdoor datasets.

To address limitations in current flame and smoke detection models, including difficulties in handling irregularities, occlusions, large model sizes, and real-time performance issues, this work introduces FS-YOLO, a lightweight attention-based model. FS-YOLO adopts an efficient architecture for feature extraction capable of capturing long-range information, overcoming issues of redundant data and inadequate global feature extraction. The model incorporates squeeze-enhanced-axial-C2f to enhance global information capture without significantly increasing computational demands. Additionally, the improved VoVNet-GSConv-cross stage partial network refines semantic information from higher-level features, reducing missed detections and maintaining a lightweight model. Compared to YOLOv8n, FS-YOLO achieves a 1.4% increase and a 1.0% increase in mAP0.5 and mAP0.5:0.95, respectively, along with a 1.3% improvement in precision and a 1.0% boost in recall. These enhancements make FS-YOLO a promising solution for flame and smoke detection, balancing accuracy and efficiency effectively.

Universal source-free domain adaptation (USFDA) aims to explore transferring domain-consistent knowledge in the presence of domain shift and category shift, without access to a source domain. Existing works mainly rely on prior domain-invariant knowledge provided by the source model, ignoring the significant discrepancy between the source and target domains. However, directly utilizing the source model will generate noisy pseudo-labels on the target domain, resulting in erroneous decision boundaries. To alleviate the aforementioned issue, we propose a two-stage USFDA approach based on prompt learning, named USDAP. Primarily, to reduce domain differences, during the prompt learning stage, we introduce a learnable prompt designed to align the target domain distribution with the source. Furthermore, for more discriminative decision boundaries, in the feature alignment stage, we propose an adaptive global-local clustering strategy. This strategy utilizes one-versus-all clustering globally to separate different categories and neighbor-to-neighbor clustering locally to prevent incorrect pseudo-label assignments at cluster boundaries. Based on the above two-stage method, target data are adapted to the classification network under the prompt’s guidance, forming more compact category clusters, thus achieving excellent migration performance for the model. We conduct experiments on various datasets with diverse category shift scenarios to illustrate the superiority of our USDAP.

Thermal radiation and texture data from two different sensor types are usually combined in the fusion of infrared and visible images for generating a single image. In recent years, convolutional neural network (CNN) based on deep learning has become the mainstream technology for many infrared and visible image fusion methods, which often extracts shallow features and ignores the role of long-range dependencies in the fusion task. However, due to its local perception characteristics, CNN can only obtain global contextual information by continuously stacking convolutional layers, which leads to low network efficiency and difficulty in optimization. To address this issue, we proposed a global context fusion network (GCFN) to model context using a global attention pool, which adopts a two-stage strategy. First, a GCFN-based autoencoder network is trained for extracting multi-scale local and global contextual features. To effectively incorporate the complementary information of the input image, a dual branch fusion network combining CNN and transformer is designed in the second step. Experimental results on a publicly available dataset demonstrate that the proposed method outperforms nine advanced methods in fusion performance on both subjective and objective metrics.

Epidermal growth factor receptor (EGFR) is a key gene for diagnosing non-small cell lung cancer (NSCLC), which affects subsequent treatment arrangements. In clinical practice, compared with invasive testing, non-invasive detection of such genes can improve diagnostic efficiency and alleviate patient suffering. We propose a non-invasive decision-support method aimed at identifying EGFR mutations or wild type in NSCLC to avoid traumatic examination. To alleviate the inherent translational invariance of convolutional neural networks (CNNs) that results in the insufficiency to capture long-range dependencies, we propose a EGFR mutations or wild-type recognition method that combines CNNs with Transformers. In addition, we develop a lightweight dot-product strategy to address computational complexity, which not only reduces computational demands but also improves the interaction of image sequence information. The proposed method demonstrates superior performance, with an area under the curve of 88.56%, accuracy (ACC) of 83.17%, specificity (Spe) of 85.34%, and sensitivity (Sen) of 80.25%. In particular, the gradient-weighted class activation mapping analysis further shows that our algorithm more effectively concentrates on activation regions relevant to the predictions. The results of the experiments demonstrated that our proposed method is effective in distinguishing between EGFR mutations and wild type, showing superior classification performance compared with other methods. Notably, it introduces a non-invasive alternative for clinical examination, which is of considerable importance for the clinical diagnosis of NSCLC.

Recent years have witnessed tremendous success of convolutional neural networks in image classification. In the training of image classification, softmax cross-entropy (SCE) loss is widely used because of its clear physical definition and concise gradient calculation. We reformulate SCE loss into a more basic form, in light of which we can understand SCE loss better and rethink the loss functions in image classification. We propose a novel loss function called another metric (AM) loss, which is different from SCE loss in form but is the same in essence. AM loss not only retains the advantages of SCE loss but also provides more gradient information at the later phase of training and has larger inter-classes distances. Extensive experiments on CIFAR-10/100, Tiny ImageNet, and three fine-grained datasets show that AM loss can outperform SCE loss, which also shows the great potential of improving deep neural network training by exploring new loss functions.

Synthetic aperture radar (SAR) images provide high-resolution observations for military targets that can be used for target recognition. A SAR target recognition method is proposed based on correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions (BIMFs) extracted by bidimensional variational mode decomposition (BVMD). As an image decomposition algorithm, BVMD represents SAR images by several BIMFs to describe the global and local properties of targets. Furthermore, non-linear correlation information entropy is adopted as a quantitative measure to choose the optimal subset from the multi-level BIMFs, which are assumed to share high correlations. In the classification phase, joint sparse representation is first employed to represent the selected BIMFs and output their corresponding reconstruction error vectors as well as decisions based on the minimum error principle. Afterward, a voting strategy is conducted on their individual decisions to reach a candidate target label. Once the voting decision is judged reliable, the classification process ends and confirms the candidate target label. Otherwise, a linear weighting algorithm is performed on the reconstruction error vectors from different BIMFs, in which the weights are decided by the voting result. Finally, the target label is determined based on the fused reconstruction errors. Experiments are conducted under different situations including the standard operating conditions and extended operating conditions from the moving and stationary target acquisition and recognition dataset. The results of the proposed method are analyzed and compared with other methods to validate its performance.

Semantic segmentation for road scenes aims to predict specific categories for each pixel in road images, playing a crucial role in various applications such as autonomous driving, scene recognition, and robotics. However, existing semantic segmentation methods face the challenge of maintaining competitive segmentation accuracy while achieving real-time inference speed. To address this, we introduce the multi-stage feature fusion network (MSF2Net) for real-time semantic segmentation of road scenes, achieving a balance between speed and accuracy. First, we develop a lightweight dilate symmetric interaction module (DSIM) to extract rich local and contextual information from images. Next, we enhance the spatial and semantic information of shallow and deep features by stacking DSIMs with different numbers and dilation rates, respectively. Finally, the position attention module supplements the global information of the image. Subsequently, a feature fusion module is utilized to integrate shallow, deep, and global features, thereby achieving real-time semantic segmentation through multi-stage feature fusion. Experimental results on multiple benchmark datasets demonstrate that MSF2Net achieves a good balance between segmentation performance and inference speed.

Dense fog often appears on highways at night and early in the morning, seriously affecting visibility and posing important traffic safety risks. The surveillance images taken during this period have poor imaging quality due to low light, which brings great challenges to visibility estimation from surveillance images. To cope with this problem, we propose a deep network with low-light enhancement capability to achieve automatic visibility estimation. The network model consists of two parts: the light enhancement sub-network and the visibility classification sub-network. The light enhancement sub-network adopts the generative adversarial network architecture to generate the light-enhanced image under the constraint of discriminator loss and self-feature retention loss. The visibility classification sub-network extracts the visual features of the enhanced image and combines them with bright channels to classify through a multi-head attention mechanism, achieving effective detection of visibility. At the same time, we construct a nighttime highway fog image dataset by collecting surveillance videos of several highways in China. Experimental results on this real-scene dataset show that the proposed algorithm achieves the highest detection accuracy, which can provide technical support for the intelligent management of the highway management department.

In harsh environments such as rain, snow, fog, and night, the road conditions are complex, and the existing road target detection algorithms are less studied, the amount of data is scarce, and the detection accuracy is not high. Aiming at the problems of less road target detection data and more interference factors in harsh environments, a road target detection method based on improved you only look once version 8n (YOLOv8n) in harsh environments is proposed. Based on the Berkeley DeepDrive-IW (BDD-IW) dataset, the improved cutout algorithm is used to simulate the occlusion of the targets to be detected and the uneven visibility of the detected images caused by the harsh environments in real scenarios to enhance the robustness. In YOLOv8n, a space-to-depth convolution (SPD-Conv) module consisting of a space-to-depth layer and a non-strided convolutional layer is added to the previous layer of each cross-stage partial Darknet53 to two-stage feature pyramid network (C2f) module of the backbone section to compensate for the loss of fine-grained image information caused by a strided convolution. On this basis, the C2f module and the parallel polarized self-attention (PPSA) mechanism are used to construct the feature extraction module cross-stage partial DarkNet53 and polarimetric self-attention to two-stage feature pyramid network and replace the C2f module in the backbone of YOLOv8n to reduce the information loss caused by more noise in harsh environments. To further improve the precision, the up-sampling part of YOLOv8n is modified to the content-aware reassembly of the feature operator to effectively aggregate the context information and expand the receptive field. The experimental results show that for BDD-IW data processed with improved cutout, compared with the original network, the improved YOLOv8n network has a small increase in the number of parameters and increases the precision by 6.0% and the mAP50 value by 5.0%, which effectively improves the performance of target detection in harsh environments.

In the natural driving state, the autonomous vehicle needs to make its driving decision, which requires precise traffic sign recognition with different distances and angles according to road traffic rules. However, the change in the observation points of autonomous cameras causes traffic signs to exhibit visual characteristics of variable size and complex backgrounds. The coupling of multiple characteristics poses severe challenges for traffic sign identification and positioning. In this paper, a multi-scale feature extraction and coordinate network based on the YOLO framework (MFEC-YOLOv7) is proposed to achieve accurate traffic sign recognition in complex situations. In more detail, the feature for traffic signs at different scales is extracted and enhanced by fusing the pooling layer and group convolution in the convolutional architecture of the backbone network, thus improving the multi-scale feature perception capability. Furthermore, the coordinate attention (CA) module is introduced to pay attention to traffic signs under multiple interference factors. And the collaborative working mechanism of the bidirectional feature pyramid is brought in the neck network, and combined with the CA module to accurately locate the position information of features, to enhance the feature extraction ability of the model. Besides, depth separable convolution is adopted to significantly reduce the number of convolution parameters and improve the efficiency without reducing the effectiveness of feature extraction. The experimental results on the public data set TT100K and the self-built data set show that compared with the YOLOv7 method, MFEC-YOLOv7 improves the detection accuracy by 19.9%, reduces the amount of parameter calculation by 9.5%, and improves the speed by about 16.9%.

We propose an approach of Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder (LCBVAE) architecture for the encoder and decoder, which is trained on the image translation of the dotted Arabic expiration dates by reconstructing the Arabic dotted expiration dates into filled-in expiration dates. We employed a customized and adapted version of the convolutional recurrent neural network (CRNN) model to meet our specific requirements and enhance its performance in our context, and then we trained the custom CRNN model with the filled-in images from the year 2019 to 2027 to extract the expiration dates and assess the model performance of LCBVAE on the expiration date recognition. The pipeline of (LCBVAE + CRNN) can be then integrated into an automated sorting system for extracting the expiry dates and sorting the products accordingly during the manufacturing stage. In addition, it can overcome the manual entry of expiration dates that can be time-consuming and inefficient at the merchants. Due to the lack of available dotted Arabic expiration date images, we created an Arabic dot-matrix TrueType font for the generation of the synthetic images. We trained the model with unrealistic synthetic dates of 60,000 images and performed the testing on a realistic synthetic date of 3000 images from the year 2019 to 2027, represented as yyyy/mm/dd. We demonstrated the significance of the latent bottleneck layer by improving the generalization when the size is increased up to 1024 in downstream transfer learning tasks for image translation. The proposed approach achieved an accuracy of 97% on the image translation using the LCBVAE architecture that can be generalized for any downstream learning tasks for image translation and reconstruction.

We propose a reversible data-hiding approach in encrypted images (RDHEI) to cope with the new security threats emerging with artificial intelligence and the Industrial Internet of Things (IIoT). This dual image-based high-capacity RDHEI technique ensures security with better embedding capacity. On the sender side, the correlations of adjacent pixels are disrupted by encryption. Hence, except for the intended receiver, no one can intercept the image. This encrypted image is used as a cover image, and pixel value ordering is applied. After that, the center folding strategy is used to form dual stego images for hiding data inside both images. Pixels with maximum and minimum values are employed for data hiding. The experimental results indicate the greater resilience and imperceptibility of the discussed technique over several current strategies. The suggested plan incorporates secret bits based on the image properties. One benefit of using the suggested technique is that the process of decrypting the image and extracting the data are independent. The recipient with just the data-hiding key may recover the concealed data without any compromise, while the receiver with only the decryption key can precisely retrieve the original image without any knowledge of the data-hiding key. This makes this scheme suitable for IIoT devices with a limited processing power for image collection.

Existing watermarking schemes typically use binary or grayscale images as watermarks, which cannot meet the needs for color image copyright protection in the Internet of Things environment. To address these issues, we propose an adaptive robust watermarking scheme in YCbCr color space, based on integer wavelet transform, Hadamard transform, and singular value decomposition (SVD). Applying the Hadamard transform to SVD can effectively enhance the anti-interference capability of watermarking technology. The scheme utilizes the rime optimization algorithm (RIME) to identify the optimal embedding strength factor for each image, thereby achieving a balance between imperceptibility and robustness. To ensure the security of the watermark information, the algorithm employs dual encryption techniques, using the scrambled generalized Gray code transform and the four-dimensional hyperchaotic Lorenz system to encrypt the watermarked image. The experimental results show that the peak signal-to-noise ratio of watermarked images is above 54 dB, the structural similarity index metric is close to 1, and a hiding capacity of 0.125 bpp has been achieved. The normalized cross-correlation of the watermarked images extracted after various attacks is above 0.9. Compared with existing watermarking schemes, this scheme has significant advantages in terms of invisibility and robustness.

Convolutional neural networks (CNNs) can use larger convolutional kernels to provide a wide receptive field, achieving long-distance context processing similar to that of transformers with fewer model parameters. However, the complex and dynamic degradation of underwater environments makes it difficult for fixed large-kernel CNNs to adaptively capture complex multi-scale features underwater and dynamically integrate a broad range of contextual information. To address these issues, we propose the integrated dynamic context network, which adopts multi-scale receptive fields and adaptively processes global contextual information. In its core module, the integrated dynamic context module is incorporated; it uses multiple different-sized kernels to capture multi-scale features and designs a dynamic selection mechanism that adaptively emphasizes the most critical spatial features while fully utilizing extensive information. The proposed dynamic selective feature fusion module promotes valuable screening of redundant features through the fusion of multi-scale feature maps derived from the encoder and the previous layer decoder. Extensive experimental results verify the superior performance of the proposed method in addressing these challenges.

Underwater image enhancement is challenging due to the complex and variable degradations induced by environmental disturbances. While deep learning methods recently have achieved substantial progress in image enhancement, their performance is still hampered by the limited understanding of underwater image characteristics. To address this, we propose the hierarchical decomposition-based underwater image enhancement network (HDNet), which tackles key elements of underwater image degradation. Our model employs macroscopic channel decomposition and microscopic pixel decomposition to correct color distortion from light attenuation and restore detail loss from scattering. In the pixel decomposition process, HDNet interprets pixels as perturbed light signals and refines image details by representing and modulating these signals, emulating the principles of the Fourier transform. In addition, we introduce an auxiliary gradient guidance strategy to mitigate the effects of poor reference images during training. HDNet demonstrates good performance across multiple datasets. Notably, on the underwater image enhancement dataset, a real-world underwater dataset, HDNet achieves a peak signal-to-noise ratio of 24.158 dB and a structural similarity index of 0.923, outperforming many previous state-of-the-art models. Its low parameter count and computational efficiency make it suitable for practical, real-world applications.

Facial expression recognition (FER) is crucial for understanding and assessing human emotional states. However, in practical applications, due to the complexity and diversity of facial expressions, traditional self-supervised contrastive learning methods are often difficult to extract fine-grained expression features. To address this problem, we propose an attention-guided self-supervised distilled contrastive learning method for FER, which transfers the expression differential information learned by the teacher network to the student network by introducing attention-guided knowledge distillation in self-supervised contrastive learning. Specifically, we propose attention-guided joint feature distillation to strengthen the feature representation capability of the student network by guiding the student network through feature learning with joint attention-weighted features and comparison query vectors. In addition, to further utilize the key information in the teacher’s features, the facial key feature guidance is also proposed to make the student more focused on learning the key features extracted from the teacher’s network. These advances lead to significant performance improvements, showcasing the robustness of our method. Our method obtains excellent results of 76.83% on the Real-world Affective Face Database and 62.04% on the FER-2013 datasets, respectively, demonstrating its effectiveness in capturing subtle emotional expressions and advancing the field of self-supervised FER.

We propose a joint scrambling and diffusion image encryption algorithm based on RNA theory and row–column–level operation (JSDRR), encompassing the following procedures: initially, pseudo-random sequences are generated utilizing a six-dimensional hyper-chaotic system, which serves to augment the randomization in the image encryption process. Subsequently, the theory of RNA is integrated with the row–column–level cryptographic approach for combined scrambling and diffusion, thereby augmenting the efficacy of the proposed algorithm. Finally, we elucidate a cutting-edge joint scrambling and diffusion image encryption algorithm, designed to withstand a multiplicity of attacks. Employing simulation and comparative analytical methodologies, it is demonstrated that the JSDRR exhibits robustness against a varied spectrum of attacks with commendable efficiency.

Relative pose estimation is crucial for various computer vision applications, including robotic and autonomous driving. Current methods primarily depend on selecting and matching feature points prone to incorrect matches, leading to poor performance. Consequently, relying solely on point-matching relationships for pose estimation is a huge challenge. To overcome these limitations, we propose a geometric correspondence graph neural network that integrates point features with extra structured line segments. This integration of matched points and line segments further exploits the geometry constraints and enhances model performance across different environments. We employ the dual-graph module and feature-weighted fusion module to aggregate geometric and visual features effectively, facilitating complex scene understanding. We demonstrate our approach through extensive experiments on the DeMoN and Karlsruhe Institute of Technology and Toyota Technological Institute Odometry datasets. The results show that our method is competitive with state-of-the-art techniques.