DFGAN: Image Deblurring through Fusing Light-Weight Attention and Gradient-Based Filters

Abstract

Recovering a latent sharp image from a spatially variant blurred image is a challenging task in the field of computer vision especially in blind image deblurring, where the source of the blur kernel is unknown and may vary. To remove the intricate motion blur in the images, recently deep learning-based methods perform latent clean image recovery without the need of knowing the blur kernel explicitly. Unlike conventional blind deblurring methods that assume the blur to be spatially invariant across the image. However, simply stacking convolution layers in deep multi-scale networks does not guarantee the complete removal of motion blur in the images and may lead to a poor performance for blind image deblurring task. Thus, we propose a GAN-based approach for single image blind motion deblurring task in an end-to-end manner, for simplicity its called DeblurFusedGAN (DFGAN). The proposed method improves the performance for image deblurring task by fusing the light-weight attention (LSA) mechanism and gradient-based filters in the generator network. Furthermore, we show the sophisticated performance of our proposed approach both qualitatively and quantitatively in comparison with the other state-of-the-art methods.