Deep Networks for Image Super-Resolution with Sparse Prior

Understand

Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems.

• For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models.
• The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem.
• In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results.