In this paper,we present an extensive review on deep learning methods for image restoration tasks. Deep learning techniques,led by convolutional neural networks,have received a great deal of attention in almost all areas of image processing,especially in image classification. However,image restoration is a fundamental and challenging topic and plays significant roles in image processing,understanding and representation. It typically addresses image deblurring,denoising,dehazing and super-resolution. There are substantial differences in the approaches and mechanisms in deep learning methods for image restoration. Discriminative learning based methods are able to deal with issues of learning a restoration mapping function effectively,while optimisation models based methods can further enhance the performance with certain learning constraints. In this paper,we offer a comparative study of deep learning techniques in image denoising,deblurring,dehazing,and super-resolution,and summarise the principles involved in these tasks from various supervised deep network architectures,residual or skip connection and receptive field to unsupervised autoencoder mechanisms. Image quality criteria are also reviewed and their roles in image restoration are assessed. Based on our analysis,we further present an efficient network for deblurring and a couple of multi-objective training functions for super-resolution restoration tasks. The proposed methods are compared extensively with the state-of-the-art methods with both quantitative and qualitative analyses. Finally,we point out potential challenges and directions for future research.

研究意义

自上个世纪以来，图像恢复一直是数字图像处理的长期研究课题[1-5]，近年来仍然是一个活跃的课题。图像恢复这是一个典型的逆问题，旨在从退化观察中恢复潜在的清洁图像。多维退化观察 (multidimensional
degraded observations) 和恢复图像之间的无限可能映射决定了这一点逆问题 (inverse problems) 不适定性 (illposed nature)<