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。

图像复原中的同时保真与正则化学习

Simultaneous Fidelity and Regularization Learning for Image Restoration

We present an effective blind image deblurring method based on a data-driven
discriminative prior.Our work is motivated by the fact that a good image prior
should favor clear images over blurred images.In this work, we formulate the
image prior as a binary classifier which can be achieved by a deep
convolutional neural network (CNN).The learned prior is able to distinguish
whether an input image is clear or not.Embedded into the maximum a posterior
(MAP) framework, it helps blind deblurring in various scenarios, including
natural, face, text, and low-illumination images.However, it is difficult to
optimize the deblurring method with the learned image prior as it involves a
non-linear CNN.Therefore, we develop an efficient numerical approach based on
the half-quadratic splitting method and gradient decent algorithm to solve the
proposed model.Furthermore, the proposed model can be easily extended to
non-uniform deblurring.Both qualitative and quantitative experimental results
show that our method performs favorably against state-of-the-art algorithms as
well as domain-specific image deblurring approaches.

本文提出了一种基于数据驱动的判别性先验的有效盲图像去模糊方法，通过深度卷积神经网络学习得到的固定分类器可以帮助在不同场景下的图像去模糊，同时使用半二次分离法和梯度下降算法解决非线性问题。