  Deep learning methods have witnessed the great progress in image restoration
with specific metrics (e.g., PSNR, SSIM). However, the perceptual quality of
the restored image is relatively subjective, and it is necessary for users to
control the reconstruction result according to personal preferences or image
characteristics, which cannot be done using existing deterministic networks.
This motivates us to exquisitely design a unified interactive framework for
general image restoration tasks. Under this framework, users can control
continuous transition of different objectives, e.g., the perception-distortion
trade-off of image super-resolution, the trade-off between noise reduction and
detail preservation. We achieve this goal by controlling the latent features of
the designed network. To be specific, our proposed framework, named
Controllable Feature Space Network (CFSNet), is entangled by two branches based
on different objectives. Our framework can adaptively learn the coupling
coefficients of different layers and channels, which provides finer control of
the restored image quality. Experiments on several typical image restoration
tasks fully validate the effective benefits of the proposed method. Code is
available at https://github.com/qibao77/CFSNet.
