Inter-Task Weaving in Image Enhancement: From a New Unified Architecture to a Better Meta-Representation Learning

摘要

Image enhancement is a classical and enduring challenge in computer vision, seeking to produce high-quality images from corrupted observations. Unlike existing methods that target specific tasks, this work focuses on endowing the model with generic inductive capabilities, enabling fast adaptation to previously unseen enhancement tasks. Specifically, we investigate the inter-task weaving from both structural and parametric perspectives. Structurally, we establish inter-task weaving under a Hadamard view by designing a unified architecture called Degradation Unraveling Network (DUNet) tailored for diverse enhancement tasks, which incorporates a progressive degradation unraveling mechanism for fine-grained enhancement. Parametrically, we reveal the task-agnostic nature of degradation estimation parameters and treat them as meta-representations. A Bilevel Purify Modeling (BPM) framework is then proposed to reinforce their latent unified representation, where only the degradation-related parameters are optimized as meta-representations. Based on this design, a task-aware adaptation solution is further introduced, only the remaining parameters are allowed to be fine-tuned efficiently and enabling fast task adaptation. Extensive performance evaluations on three representative image enhancement tasks demonstrate the effectiveness and superiority of our method. The adaptability of our method is further verified by a series of algorithm analyses.