  Image signal processing (ISP) pipeline plays a fundamental role in digital
cameras, which converts raw Bayer sensor data to RGB images. However,
ISP-generated images usually suffer from imperfections due to the compounded
degradations that stem from sensor noises, demosaicing noises, compression
artifacts, and possibly adverse effects of erroneous ISP hyperparameter
settings such as ISO and gamma values. In a general sense, these ISP
imperfections can be considered as degradations. The highly complex mechanisms
of ISP degradations, some of which are even unknown, pose great challenges to
the generalization capability of deep neural networks (DNN) for image
restoration and to their adaptability to downstream tasks. To tackle the
issues, we propose a novel DNN approach to learn degradation-independent
representations (DiR) through the refinement of a self-supervised learned
baseline representation. The proposed DiR learning technique has remarkable
domain generalization capability and consequently, it outperforms
state-of-the-art methods across various downstream tasks, including blind image
restoration, object detection, and instance segmentation, as verified in our
experiments.
