Most existing non-blind restoration methods are based on the assumption that
a precise degradation model is known. As the degradation process can only be
partially known or inaccurately modeled, images may not be well restored. Rain
streak removal and image deconvolution with inaccurate blur kernels are two
representative examples of such tasks. For rain streak removal, although an
input image can be decomposed into a scene layer and a rain streak layer, there
exists no explicit formulation for modeling rain streaks and the composition
with scene layer. For blind deconvolution, as estimation error of blur kernel
is usually introduced, the subsequent non-blind deconvolution process does not
restore the latent image well. In this paper, we propose a principled algorithm
within the maximum a posterior framework to tackle image restoration with a
partially known or inaccurate degradation model. Specifically, the residual
caused by a partially known or inaccurate degradation model is spatially
dependent and complexly distributed. With a training set of degraded and
ground-truth image pairs, we parameterize and learn the fidelity term for a
degradation model in a task-driven manner. Furthermore, the regularization term
can also be learned along with the fidelity term, thereby forming a
simultaneous fidelity and regularization learning model. Extensive experimental
results demonstrate the effectiveness of the proposed model for image
deconvolution with inaccurate blur kernels, deconvolution with multiple
degradations and rain streak removal.

本文介绍了一种用于图像恢复的方法，在部分已知或精度不高的退化模型下，我们在最大后验框架中提出了一种系统的算法来处理。通过一组残缺和完整的图像对进行参数化和学习，同时学习正则化项，形成一个同时的保真度和正则化学习模型，有效地处理了图像去噪和去雨痕等问题。