Rain Removal via Shrinkage-Based Sparse Coding and Learned Rain Dictionary

Understand

This paper introduces a new rain removal model based on the shrinkage of the sparse codes for a single image.

• Recently, dictionary learning and sparse coding have been widely used for image restoration problems.
• These methods can also be applied to the rain removal by learning two types of rain and non-rain dictionaries and forcing the sparse codes of the rain dictionary to be zero vectors.
• However, this approach can generate unwanted edge artifacts and detail loss in the non-rain regions.