Images taken in hazy weather are susceptible to the effects of haze, resulting in blurred images and low contrast to the extent that important information is lost in the image. Therefore, this is necessary to dehaze haze images, process the image information and ensure the normal operation of other computer vision tasks. Traditional deep learning-based image dehazing methods often suffer from uneven haze removal, colour bias and loss of detail. To solve this problem, this paper proposes a single image dehazing method (IMNet) based on pyramidal input for image dehazing. The network is divided into three modules: an intensive feature extraction module, a pyramid input branch and a detail deepening module. This paper uses two loss functions in combination, which can help preserve texture details more effectively. Experimental results have shown that IMNet outperforms other dehazing algorithms in terms of metrics and visual effects.
Obtaining change information in different periods from a pair of registered satellite remote sensing images is of great significance to urban planning, so change detection (CD) technology has attracted extensive attention in recent years. In recent years, convolutional neural networks have set off a boom in many artificial intelligence research fields because of their excellent feature extraction performance. However, the common convolution operation mainly focuses on the abstraction of the semantic information of the features, which often leads to the details of the features being ignored and thus affects the final accuracy. For example, the contour details of changing objects and the structural information of small objects are often lost. We propose a Siamese network that enhances contour and structural details to achieve higher-accuracy CD tasks for bitemporal remote sensing images. In this network, we propose an efficient contour-enhanced convolutional block that is based on the reparameterization technique. The contour-enhanced convolutional block strengthens the extraction of structural and contour features by integrating different branches. In addition, inspired by NestedUNet and to better preserve the original location information of features, we use a dense connection as the feature extractor to obtain refined features of bitemporal images. After that, we use a difference module to calculate the change characteristics of the dual-time image, and we use atrous spatial pyramid pooling and enhanced spatial attention to further refine the obtained change characteristics. We conduct extensive experiments on three different datasets to verify the effectiveness of our model. Experimental results show that our method outperforms state-of-the-art methods in both overall accuracy and visualization details.
Change detection (CD) is the operation of quantitatively analyzing the surface changes of a phenomenon or objects over two different times. Lately, CD based on deep learning has developed to become more and more powerful, and convolutional neural networks (CNNs) have dominated the field of remote sensing (RS) CD. In particular, in many fields of computer vision, neural networks based on U-Net network and skip connections have been generally used. However, despite the excellent performance achieved by CNN, it does not learn global and long-range semantic information interaction well due to the locality of convolutional operations. The recently proposed Swin-UNet in the field of medical image segmentation achieved excellent results, which is a U-Net-like pure transformer. In the face of the challenge of segmentation accuracy, the Swin transformer has demonstrated strong capabilities. The Swin transformer block (STB) consists of residual connected STBs used in SwinIR to enhanced training stability. We began to try to incorporate them into our network for RS CD. Finally, we propose a transformer-based multi-scale feature fusion model (TMFF), including decoder, encoder, and skip connection structure, for RS image CD. We modify the original U-Net architecture so that it can better aggregate semantic features at all levels. Our proposed TMFF achieves impressive results through experiments on three datasets;
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