Abstract
Blind image restoration processors based on convolutional neural network (CNN) are intensively researched because of their high performance. However, they are too sensitive to the perturbation of the degradation model. They easily fail to restore the image whose degradation model is slightly different from the trained degradation model. In this paper, we propose a non-blind CNN-based image restoration processor, aiming to be robust against a perturbation of the degradation model compared to the blind restoration processor. Experimental comparisons demonstrate that the proposed non-blind CNN-based image restoration processor can robustly restore images compared to existing blind CNN-based image restoration processors.
Abstract (translated)
基于卷积神经网络（CNN）的盲图像恢复处理器因其高性能而得到了深入的研究。然而，它们对降解模型的扰动过于敏感。他们很容易无法恢复其退化模型与训练的退化模型略有不同的图像。在本文中，我们提出了一种基于CNN的非盲基图像恢复处理器，旨在与盲恢复处理器相比，能够抵抗劣化模型的扰动。实验比较表明，与现有的基于盲CNN的图像恢复处理器相比，所提出的基于CNN的非盲基图像恢复处理器可以稳健地恢复图像。
URL
https://arxiv.org/abs/1809.03757
