  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. The additional module, namely AdaFM layer, performs
channel-wise feature modification, and can adapt a model to another restoration
level with high accuracy. By simply tweaking an interpolation coefficient, the
intermediate model - AdaFM-Net could generate smooth and continuous restoration
effects without artifacts. Extensive experiments on three image restoration
tasks demonstrate the effectiveness of both model training and modulation
testing. Besides, we carefully investigate the properties of AdaFM layers,
providing a detailed guidance on the usage of the proposed method.
