Learning a Shared Representation for Low-Level Vision via Degradation Understanding

Low-level vision tasks such as image quality assessment (IQA) and restoration rely on common evidence about image perturbations, yet learned degradation representations are usually architecture- or task-specific. We propose Degradation Understanding as a supervisory task for learning a shared low-level representation. It predicts a hierarchical state over degradation type, composition, parameter keys, and continuous values, exposing a model-agnostic interface without assuming recovery of the true imaging history. To support this task, we construct DU-200K from 160,000 synthetic samples with structured supervision and 40,000 real-captured samples with restoration-grounded feedback. We design a Degradation Understanding vision-language model (DU-VLM) that learns structured prediction via supervised fine-tuning (SFT) and outcome alignment via Group Relative Policy Optimization (GRPO). We evaluate our method across multiple downstream tasks. For IQA task, our representation yields a 29.9% average improvement in SRCC across five datasets. On real-captured data, explicit degradation prompts predicted by our method improve PSNR by up to 1.74 dB over generic text prompts across state-of-the-art image-editing models. On entirely unseen and challenging real-world images, our system remains highly competitive among restoration systems based on diverse degradation-representation paradigms, supporting transfer across tasks, interfaces, backbones, and real scenes.