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.gitattributes

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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

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LICENSE.md

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+Pi-Lab License 1.0
+
+Copyright 2024 Pi-Lab
+
+Redistribution and use for non-commercial purpose in source and 
+binary forms, with or without modification, are permitted provided 
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright 
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright 
+   notice, this list of conditions and the following disclaimer in 
+   the documentation and/or other materials provided with the 
+   distribution.
+
+3. Neither the name of the copyright holder nor the names of its 
+   contributors may be used to endorse or promote products derived 
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
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+In the event that redistribution and/or use for commercial purpose in 
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+please contact the contributor(s) of the work.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+  - low-level-vision
+  - all-in-one image-restoration
+language:
+  - en
+pipeline_tag: image-to-image
+model-index:
+  - name: RAM / RAM++
+    results:
+      - task:
+          type: image-to-image
+          name: All-in-One Image Restoration
+        dataset:
+          name: placeholder
+          type: image
+        metrics:
+          - name: PSNR
+            type: psnr
+            value: 0.0
+---
+This is the official pretrained models for the paper.  
+>**Restore Anything with Masks：Leveraging Mask Image Modeling for Blind All-in-One Image Restoration**<br>  [Chujie Qin](https://github.com/Dragonisss), [Ruiqi Wu](https://rq-wu.github.io/), [Zikun Liu](), [Xin Lin](https://linxin0.github.io/), [Chunle Guo](https://scholar.google.com/citations?user=RZLYwR0AAAAJ&hl=en), [Hyun Hee Park](s), [Chongyi Li<sup>†</sup>](https://li-chongyi.github.io/)<br/>
+> ( † indicates corresponding author )<br/>
+> In ECCV 2024, \[[HomePage](https://rq-wu.github.io/projects/RAM/index.html)\], \[[Paper Link](https://arxiv.org/abs/2409.19403v1)\]
+
+> **RAM++: <u>R</u>obust Representation Learning via <u>A</u>daptive <u>M</u>ask for All-in-One Image Restoration**<br>
+> [Zilong Zhang<sup>*</sup>](https://github.com/Zilong-Zhang003), [Chujie Qin<sup>*</sup>](https://github.com/DragonisCV), [Chunle Guo](https://mmcheng.net/clguo/), [Yong Zhang](), [Chao Xue](), [Ming-Ming Cheng](https://mmcheng.net/cmm/), [Chongyi Li<sup>†</sup>](https://li-chongyi.github.io/)<br/>
+> (<sup>*</sup>indicates equal contribution; <sup>†</sup> indicates corresponding author)<br/>
+> arxiv preprint, \[[HomePage](https://zilong-zhang003.github.io/RAM2.0/)\], \[[Paper Link](https://arxiv.org/abs/2509.12039)\]
+
+
+# Model description
+## RAM
+ This method is architecture-agnostic and can be trained with any model. \
+ Here we provide the pre-trained and fine-tuned weights for two representative models: <strong>[PromptIR](https://github.com/va1shn9v/PromptIR)</strong> and <strong>[SwinIR](https://github.com/JingyunLiang/SwinIR)</strong>.
+## RAM_plus
+<strong>AdaSAM</strong> is a ViT-based, pixel-level mask generator. It analyzes correlations between image tokens and applies masks to regions that are semantically and texturally rich.
+
+<strong>RestormerWoSkip</strong> is built on <strong>[Restormer](https://github.com/swz30/Restormer)</strong>; it differs by removing the long-range residual connections.
+
+<strong>RestormerRFR</strong> regularizes via an efficient feature-fusion strategy that leverages DINOv2’s semantic consistency and degradation invariance.
+
+<strong>Different folders</strong> contain model weights trained under configurations with different numbers of tasks.
+
+# How to use
+For full instructions and runnable scripts, see the [code repository](https://github.com/DragonisCV/RAM/) 
+## RAM
+### Pre-training：
+```python
+mask, mask_token = Random(img)   #pixel_level
+output = PromptIR(img, mask, mask_token) 
+```
+### Fine-tuning:
+```python  
+output = PromptIR(img, mask=None, mask_token=None)
+```
+## RAM_plus
+### Pre-training：
+```python
+mask, mask_token = AdaSAM(img)   
+output = RestormerWoSkip(img, mask, mask_token)
+```
+### Fine-tuning:
+```python
+dino_features = DINOv2(img)   
+output = RestormerRFR(img, mask=None, mask_token=None, dino_features)
+```
+
+# Citation
+If you find our repo useful for your research, please consider citing our paper:
+```bibtex
+@inproceedings{qin2024restore,
+  title={Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration},
+  author={Qin, Chu-Jie and Wu, Rui-Qi and Liu, Zikun and Lin, Xin and Guo, Chun-Le and Park, Hyun Hee and Li, Chongyi},
+  booktitle={European Conference on Computer Vision},
+  pages={364--380},
+  year={2024},
+  organization={Springer}
+}
+
+@misc{zhang2025ramrobustrepresentationlearning,
+      title={RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration}, 
+      author={Zilong Zhang and Chujie Qin and Chunle Guo and Yong Zhang and Chao Xue and Ming-Ming Cheng and Chongyi Li},
+      year={2025},
+      eprint={2509.12039},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2509.12039}, 
+}
+```

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