Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

In image restoration tasks, learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. 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. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy.