RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration

• URL: http://arxiv.org/abs/2509.12039v1
• Date: Mon, 15 Sep 2025 15:24:15 GMT
• Title: RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration
• Authors: Zilong Zhang, Chujie Qin, Chunle Guo, Yong Zhang, Chao Xue, Ming-Ming Cheng, Chongyi Li,
• Abstract summary: RAM++ is a two-stage framework for all-in-one image restoration.<n>It integrates high-level semantic understanding with low-level texture generation.<n>It addresses the limitations of existing degradation-oriented methods in extreme scenarios.
• Score: 94.49712266736141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents Robust Representation Learning via Adaptive Mask (RAM++), a two-stage framework for all-in-one image restoration. RAM++ integrates high-level semantic understanding with low-level texture generation to achieve content-oriented robust restoration. It addresses the limitations of existing degradation-oriented methods in extreme scenarios (e.g., degradations strongly coupled with image structures). RAM++ also mitigates common challenges such as unbalanced performance across tasks, overfitting to seen degradations, and weak generalization to unseen ones through three key designs: 1) Adaptive Semantic-Aware Mask (AdaSAM): a pretraining strategy that applies pixel-level masks to semantically rich and textured regions. This design enables the network to learn both generative priors and image content priors from various degradations. 2) Mask Attribute Conductance (MAC): a selective fine-tuning strategy that adjusts the layers with higher contributions to bridge the integrity gap between masked pretraining and full-image fine-tuning while retaining learned priors. 3) Robust Feature Regularization (RFR): a strategy that leverages DINOv2's semantically consistent and degradation-invariant representations, together with efficient feature fusion, to achieve faithful and semantically coherent restoration. With these designs, RAM++ achieves robust, well-balanced, and state-of-the-art performance across seen, unseen, extreme, and mixed degradations. Our code and model will be released at https://github.com/DragonisCV/RAM

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