A novel method for generating infrared targets is proposed, based on conditional diffusion inpainting. Firstly, we introduce a transformation framework designed to translate detection annotations from text to image, serving as the conditional input for the diffusion inpainting convolutional neural network. Secondly, a novel multi-scale image-condition Unet is designed as the mainframe of diffusion model. Finally, we conduct numerous direct and indirect evaluation experiments to assess the proposed algorithm. Experimental results demonstrate that the proposed algorithm generates high-quality infrared targets. Furthermore, as an augmentation, the generated images significantly enhance the detection accuracy of few-shot thermal infrared targets.
Most recent image deblurring methods only use valid information found in input image as the clue to fill the deblurring region. These methods usually have the defects of insufficient prior information and relatively poor adaptiveness. Patch-based method not only uses the valid information of the input image itself, but also utilizes the prior information of the sample images to improve the adaptiveness. However the cost function of this method is quite time-consuming and the method may also produce ringing artifacts. In this paper, we propose an improved non-blind deblurring algorithm based on learning patch likelihoods. On one hand, we consider the effect of the Gaussian mixture model with different weights and normalize the weight values, which can optimize the cost function and reduce running time. On the other hand, a post processing method is proposed to solve the ringing artifacts produced by traditional patch-based method. Extensive experiments are performed. Experimental results verify that our method can effectively reduce the execution time, suppress the ringing artifacts effectively, and keep the quality of deblurred image.
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