Deblurring And Super-Resolution Using Deep Gated Fusion Attention Networks For Face Images
Chao-Hsun Yang, Long-Wen Chang
Abstract
Image deblurring and super-resolution are very important in image processing such as face verification. However, when in the outdoors, we often get blurry and low resolution images. To solve the problem, we propose a deep gated fusion attention network (DGFAN) to generate a high resolution image without blurring artifacts. We extract features from two task-independent structures for deburring and super-resolution to avoid the error propagation in the cascade structure of deblurring and super-resolution. We also add an attention module in our network by using channel-wise and spatial-wise features for better features and propose an edge loss function to make the model focus on facial features like eyes and nose. DGFAN performs favorably against the state-of-arts methods in terms of PSNR and SSIM. Also, using the clear images generated by DGFAN can improve the accuracy on face verification.