  State-of-the-art approaches toward image restoration can be classified into
model-based and learning-based. The former - best represented by sparse coding
techniques - strive to exploit intrinsic prior knowledge about the unknown
high-resolution images; while the latter - popularized by recently developed
deep learning techniques - leverage external image prior from some training
dataset. It is natural to explore their middle ground and pursue a hybrid image
prior capable of achieving the best in both worlds. In this paper, we propose a
systematic approach of achieving this goal called Structured Analysis Sparse
Coding (SASC). Specifically, a structured sparse prior is learned from
extrinsic training data via a deep convolutional neural network (in a similar
way to previous learning-based approaches); meantime another structured sparse
prior is internally estimated from the input observation image (similar to
previous model-based approaches). Two structured sparse priors will then be
combined to produce a hybrid prior incorporating the knowledge from both
domains. To manage the computational complexity, we have developed a novel
framework of implementing hybrid structured sparse coding processes by deep
convolutional neural networks. Experimental results show that the proposed
hybrid image restoration method performs comparably with and often better than
the current state-of-the-art techniques.

Image and Video Processing;Computer Vision and Pattern Recognition