Learning Deep CNN Denoiser Prior for Image Restoration

Understand

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision.

• Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task.
• Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring).
• Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning.