Non-blind image deblurring is typically formulated as a linear least-squares
problem regularized by natural priors on the corresponding sharp picture's
gradients, which can be solved, for example, using a half-quadratic splitting
method with Richardson fixed-point iterations for its least-squares updates and
a proximal operator for the auxiliary variable updates. We propose to
precondition the Richardson solver using approximate inverse filters of the
(known) blur and natural image prior kernels. Using convolutions instead of a
generic linear preconditioner allows extremely efficient parameter sharing
across the image, and leads to significant gains in accuracy and/or speed
compared to classical FFT and conjugate-gradient methods. More importantly, the
proposed architecture is easily adapted to learning both the preconditioner and
the proximal operator using CNN embeddings. This yields a simple and efficient
algorithm for non-blind image deblurring which is fully interpretable, can be
learned end to end, and whose accuracy matches or exceeds the state of the art,
quite significantly, in the non-uniform case.

本篇论文提出了一种用于图像去模糊的简单而高效的算法，其通过预处理 Richardson 解算器，使用模糊核和自然图像先验核的近似逆滤波器，以卷积的形式代替通用的线性预处理器，可以实现图像间高效的参数共享；同时利用 CNN 嵌入学习预处理器与辅助变量的 proximal operator，进一步提高了算法的鲁棒性与准确性，使其成为非盲目图像去模糊领域中性能更出色的算法。