2018/12/03 by Masanori Suganuma, Xing Liu, Suganuma, Masanori +3 · 3 citations
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Image and Signal Denoising Methods
paper · pdf · doi:10.48550/arxiv.1812.00733
openalex publication_date 2018/12/03 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
Many studies have been conducted so far on image restoration, the problem of\nrestoring a clean image from its distorted version. There are many different\ntypes of distortion which affect image quality. Previous studies have focused\non single types of distortion, proposing methods for removing them. However,\nimage quality degrades due to multiple factors in the real world. Thus,\ndepending on applications, e.g., vision for autonomous cars or surveillance\ncameras, we need to be able to deal with multiple combined distortions with\nunknown mixture ratios. For this purpose, we propose a simple yet effective\nlayer architecture of neural networks. It performs multiple operations in\nparallel, which are weighted by an attention mechanism to enable selection of\nproper operations depending on the input. The layer can be stacked to form a\ndeep network, which is differentiable and thus can be trained in an end-to-end\nfashion by gradient descent. The experimental results show that the proposed\nmethod works better than previous methods by a good margin on tasks of\nrestoring images with multiple combined distortions.\n