Mixed distortion image enhancement method based on joint of deep residuals learning and reinforcement learning

The images distortion leads to the loss of image information and the degradation of perceptual quality. To solve this problem, we investigate a novel mixed distortion image enhancement method based on the parallel network combines deep residual and reinforcement learning. The no-reference image quality assessment algorithm is used to determine the type and level of distorted images accurately. According to the type of distortion, the mixed distortion images enter one of the subsequent parallel joint learning networks automatically. In the joint learning framework, we prepare different residual networks to handle specialized restoration assignments including deblurring, denoising, or JPEG compression. Simultaneously, reinforcement learning then learns a policy to select the next best restoration tasks to progressively restore the quality of a corrupted image. Our method is capable of restoring images corrupted with complex mixed distortions in a more parameter-efficient manner in comparison to conventional networks. The extensive experiments on synthetic and real-world images validate the superior performances of the proposed method.