Constrained Regularization by Denoising with Automatic Parameter Selection

Understand

Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors.

• Since the regularization parameter in the traditional RED does not have any physical interpretation, it does not provide an approach for automatic parameter selection.
• This letter addresses this issue by introducing the Constrained Regularization by Denoising (CRED) method that reformulates RED as a constrained optimization problem where the regularization parameter corresponds directly to the amount of noise in the measurements.
• The solution to the constrained problem is solved by designing an efficient method based on alternating direction method of multipliers (ADMM).