Self-aware attentional image demotion fuzzy algorithm based on conditional generative adversarial network
Wei Ping He · International Conference on Electronic Information Technology (EIT 2022) · 2022
Image has been widely used in various fields, but due to camera motion or scene change, the image will form different degrees of non-uniform motion blur. For image motion blur, the existing motion blur method based on conditional generative adversarial network still has a lot of room for improvement in quality and efficiency. As a result, In this paper, we propose a self-aware Attention Motion Deblurring Using Conditional Adversarial Networks, which is based on Conditional generative Adversarial Networks. (SAD-GAN), adding Self-aware Attention (SA)and Cascaded Parallel Dilated Convolution (CPDC) to the generator. The discriminator uses the ideas of global discriminator, local discriminator and relative discriminator, and the whole network works closely together to capture the context fuzzy information more accurately, so as to achieve high quality deblurring. Experimental results on existing public data sets show that the proposed method performs better than other advanced methods in image de-motion-fuzzy structure similarity (PSNR) and peak to noise ratio (SSIM) quantitative indexes.