Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

Understand

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods.

• Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels.
• This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters.
• We make a step forward by proposing a unified CNN framework that consists of few additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level.