{"id":13701654,"url":"https://github.com/google-research/maxim","last_synced_at":"2025-07-16T20:31:17.247Z","repository":{"id":37490606,"uuid":"470020023","full_name":"google-research/maxim","owner":"google-research","description":"[CVPR 2022 Oral] Official repository for \"MAXIM: Multi-Axis MLP for Image Processing\". 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Notebook"],"sub_categories":[],"readme":"[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/deblurring-on-hide-trained-on-gopro)](https://paperswithcode.com/sota/deblurring-on-hide-trained-on-gopro?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/deblurring-on-gopro)](https://paperswithcode.com/sota/deblurring-on-gopro?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/deblurring-on-realblur-j-1)](https://paperswithcode.com/sota/deblurring-on-realblur-j-1?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/deblurring-on-realblur-r)](https://paperswithcode.com/sota/deblurring-on-realblur-r?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/deblurring-on-realblur-j-trained-on-gopro)](https://paperswithcode.com/sota/deblurring-on-realblur-j-trained-on-gopro?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/deblurring-on-realblur-r-trained-on-gopro)](https://paperswithcode.com/sota/deblurring-on-realblur-r-trained-on-gopro?p=maxim-multi-axis-mlp-for-image-processing)\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/low-light-image-enhancement-on-lol)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lol?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/photo-retouching-on-mit-adobe-5k)](https://paperswithcode.com/sota/photo-retouching-on-mit-adobe-5k?p=maxim-multi-axis-mlp-for-image-processing)\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/single-image-deraining-on-rain100h)](https://paperswithcode.com/sota/single-image-deraining-on-rain100h?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/single-image-deraining-on-rain100l)](https://paperswithcode.com/sota/single-image-deraining-on-rain100l?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/single-image-deraining-on-test100)](https://paperswithcode.com/sota/single-image-deraining-on-test100?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/single-image-deraining-on-test2800)](https://paperswithcode.com/sota/single-image-deraining-on-test2800?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/single-image-deraining-on-test1200)](https://paperswithcode.com/sota/single-image-deraining-on-test1200?p=maxim-multi-axis-mlp-for-image-processing)\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/image-denoising-on-sidd)](https://paperswithcode.com/sota/image-denoising-on-sidd?p=maxim-multi-axis-mlp-for-image-processing)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/maxim-multi-axis-mlp-for-image-processing/image-denoising-on-dnd)](https://paperswithcode.com/sota/image-denoising-on-dnd?p=maxim-multi-axis-mlp-for-image-processing)\n\n# MAXIM: Multi-Axis MLP for Image Processing (CVPR 2022 Oral, Best Paper Nomination)\n\n[![Paper](https://img.shields.io/badge/arXiv-Paper-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2201.02973)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/maxim/blob/master/colab_inference_demo.ipynb)\n[![slides](https://img.shields.io/badge/Presentation-Slides-B762C1)](https://docs.google.com/presentation/d/1NKT0PZrpmsCZTdgvsZztfNUJJ9Bvlr1r/edit?usp=sharing\u0026ouid=103274492054041370194\u0026rtpof=true\u0026sd=true)\n[![Poster](https://img.shields.io/badge/Poster-Slide-87CEEB)](https://docs.google.com/presentation/d/1fd73qn_8Ymc5okFttQ3vzQm1SABbIeoI/edit?usp=sharing\u0026ouid=103274492054041370194\u0026rtpof=true\u0026sd=true)\n\nThis repo hosts the official implementation of the MAXIM models: \n\n[\"MAXIM: Multi-Axis MLP for Image Processing\"](https://arxiv.org/abs/2201.02973). CVPR 2022 Oral.\\\n[Zhengzhong Tu](https://www.linkedin.com/in/vztu/), [Hossein Talebi](https://scholar.google.com/citations?hl=en\u0026user=UOX9BigAAAAJ), [Han Zhang](https://sites.google.com/view/hanzhang), [Feng Yang](https://sites.google.com/view/feng-yang), [Peyman Milanfar](https://sites.google.com/view/milanfarhome/), [Alan Bovik](https://www.ece.utexas.edu/people/faculty/alan-bovik), and [Yinxiao Li](https://scholar.google.com/citations?user=kZsIU74AAAAJ\u0026hl=en)\\\nGoogle Research, University of Texas at Austin\n\n*Disclaimer: This is not an officially supported Google product.*\n\n**News**:\n\n- Jan 8, 2023: Released a pytorch implementation. Check it out here: [maxim-pytorch](https://github.com/vztu/maxim-pytorch/tree/main/maxim_pytorch).\n- Oct 21, 2022: MAXIM models have been ported to TensorFlow by [@sayakpaul](https://github.com/sayakpaul). Check it out here: [maxim-tf](https://github.com/sayakpaul/maxim-tf). He also created a couple of Hugging Face Spaces to allow users to quickly try out the different models:\n  * [Denoising](https://huggingface.co/spaces/sayakpaul/sidd-denoising-maxim)\n  * [Low-light enhancement](https://huggingface.co/spaces/sayakpaul/lol-enhancement-maxim)\n  * [Image retouching](https://huggingface.co/spaces/sayakpaul/fivek-retouching-maxim)\n  * [Dehazing indoors](https://huggingface.co/spaces/sayakpaul/sots-indoor-dehazing-maxim)\n  * [Dehazing outdoors](https://huggingface.co/spaces/sayakpaul/sots-outdoor-dehazing-maxim)\n  * [Image deraining](https://huggingface.co/spaces/sayakpaul/rain13k-deraining-maxim)\n  * [Image deblurring](https://huggingface.co/spaces/sayakpaul/gopro-deblurring-maxim)\n- Sep 8, 2022: our Google AI blog covering both [MaxViT](https://arxiv.org/abs/2204.01697) and [MAXIM](https://github.com/google-research/maxim) is [live](https://ai.googleblog.com/2022/09/a-multi-axis-approach-for-vision.html).\n- Apr 25, 2022: Added demos.\n  - Colab demo by [@deshwalmahesh](https://github.com/deshwalmahesh) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/maxim/blob/master/colab_inference_demo.ipynb)\n  - Replicate web demo [![Replicate](https://replicate.com/google-research/maxim/badge)](https://replicate.com/google-research/maxim).\n- Jun 22, 2022: MAXIM selected as 1 of the best paper nomination!\n- Mar 29, 2022: MAXIM selected for an oral presentation at CVPR 2022!\n- Mar 28, 2022: initial push to Github.\n- Mar 3, 2022: paper accepted to CVPR 2022!\n- Jan 9, 2022: initial uploads to [Arxiv](https://arxiv.org/abs/2201.02973)\n\n## Quick Demos\nTry the web demo for Image Denoising, Deblurring, Deraining, Dehazing and Enhancement with customized input image here [![Replicate](https://replicate.com/google-research/maxim/badge)](https://replicate.com/google-research/maxim)\n\nTry the Colab here using [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/maxim/blob/master/colab_inference_demo.ipynb)\n\n\n## Architecture\n\n![Model overview](maxim/images/overview.png)\n\n## Installation\n\nInstall dependencies:\n\n```\npip install -r requirements.txt\n```\n\nSetup project:\n\n```\npip install .\n```\n\n## Results and Pre-trained models\n\nWe provide the pre-trained models and visual results.\nPlease contact us if you have any questions or requests.\n\n| Task | Dataset | PSNR | SSIM | Model | #params | FLOPs | ckpt | outputs |\n|:---:|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|\n| Denoising | SIDD | 39.96 | 0.960 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Denoising/SIDD/) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Denoising/SIDD/) |\n| Denoising | DND  | 39.84 | 0.954 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Denoising/SIDD/) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Denoising/DND/) |\n| Deblurring | GoPro | 32.86 | 0.961 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deblurring/GoPro) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deblurring/GoPro/) |\n| Deblurring | HIDE  | 32.83 | 0.956 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deblurring/GoPro) | images \u003c!--(https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deblurring/GoPro/)--\u003e |\n| Deblurring | REDS  | 28.93 | 0.865 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deblurring/REDS) | images \u003c!--(https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deblurring/REDS/)--\u003e |\n| Deblurring | RealBlur-R | 39.45 | 0.962 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deblurring/RealBlur_R) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deblurring/RealBlur/) |\n| Deblurring | RealBlur-J | 32.84 | 0.935 | MAXIM-3S | 22.2M | 339G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deblurring/RealBlur_J) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deblurring/RealBlur/) |\n| Deraining | Rain13k | 33.24 | 0.933 | MAXIM-2S | 14.1M | 216G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deraining/Rain13k) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deraining/Rain13k/) |\n| Deraining | Raindrop | 31.87 | 0.935 | MAXIM-2S | 14.1M | 216G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Deraining/Raindrop) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Deraining/Raindrop/) |\n| Dehazing | RESIDE-Indoor | 38.11 | 0.991 | MAXIM-2S | 14.1M | 216G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Dehazing/SOTS-Indoor) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Dehazing/RESIDE-Indoor/) |\n| Dehazing | RESIDE-Outdoor | 34.19 | 0.985 | MAXIM-2S | 14.1M | 216G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Dehazing/SOTS-Outdoor) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Dehazing/RESIDE-Outdoor/) |\n| Enhancement | LOL | 23.43 | 0.863 | MAXIM-2S | 14.1M | 216G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Enhancement/LOL) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Enhancement/LOL/) |\n| Enhancement | FiveK | 26.15 | 0.945 | MAXIM-2S | 14.1M  |  216G | [ckpt](https://console.cloud.google.com/storage/browser/gresearch/maxim/ckpt/Enhancement/FiveK) | [images](https://console.cloud.google.com/storage/browser/gresearch/maxim/results/Enhancement/FiveK/) |\n\n\u003c!-- You can also download most of the training and test datasets we used on [gcloud](https://console.cloud.google.com/storage/browser/gresearch/maxim/datasets/). --\u003e\n\n## Demo\n\nFirst download corresponding checkpoints and then go ahead and run:\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eImage Denoising\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n```\npython3 maxim/run_eval.py --task Denoising --ckpt_path ${SIDD_CKPT_PATH} \\\n  --input_dir maxim/images/Denoising --output_dir maxim/images/Results --has_target=False\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eImage Deblurring\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n```\npython3 maxim/run_eval.py --task Deblurring --ckpt_path ${GOPRO_CKPT_PATH} \\\n  --input_dir maxim/images/Deblurring --output_dir maxim/images/Results --has_target=False\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eImage Deraining\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\nRain streak:\n```\npython3 maxim/run_eval.py --task Deraining --ckpt_path ${RAIN13K_CKPT_PATH} \\\n  --input_dir maxim/images/Deraining --output_dir maxim/images/Results --has_target=False\n```\n\nRain drop:\n```\npython3 maxim/run_eval.py --task Deraining --ckpt_path ${RAINDROP_CKPT_PATH} \\\n  --input_dir maxim/images/Deraining --output_dir maxim/images/Results --has_target=False\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eImage Dehazing\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\nIndoor:\n```\npython3 maxim/run_eval.py --task Dehazing --ckpt_path ${REDISE_INDOOR_CKPT_PATH} \\\n  --input_dir maxim/images/Dehazing --output_dir maxim/images/Results --has_target=False\n```\n\nOutdoor:\n```\npython3 maxim/run_eval.py --task Dehazing --ckpt_path ${REDISE_OUTDOOR_CKPT_PATH} \\\n  --input_dir maxim/images/Dehazing --output_dir maxim/images/Results --has_target=False\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eImage Enhancement\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\nLow-light enhancement:\n```\npython3 maxim/run_eval.py --task Enhancement --ckpt_path ${LOL_CKPT_PATH} \\\n  --input_dir maxim/images/Enhancement --output_dir maxim/images/Results --has_target=False\n```\n\nRetouching:\n```\npython3 maxim/run_eval.py --task Enhancement --ckpt_path ${FIVEK_CKPT_PATH} \\\n  --input_dir maxim/images/Enhancement --output_dir maxim/images/Results --has_target=False\n```\n\u003c/details\u003e\n\n## Results\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cstrong\u003eImage Denoising\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n\u003cimg src = \"https://user-images.githubusercontent.com/43280278/149262475-a73668f2-9fe1-4374-8ed3-4831acca8052.png\" width=\"400\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eImage Deblurring\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e \u003cimg src = \"https://user-images.githubusercontent.com/43280278/149261823-b77e9513-b3b5-4caf-a0eb-67bf18c2f681.png\" width=\"500\"\u003e \u003c/td\u003e\n    \u003ctd\u003e \u003cimg src = \"https://user-images.githubusercontent.com/43280278/149261858-24664c33-dc8a-47c3-b84d-ba64b1c05937.png\" width=\"500\"\u003e \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cp align=\"center\"\u003e\u003cb\u003eSynthetic blur\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cp align=\"center\"\u003e\u003cb\u003eRealistic blur\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eImage Deraining\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e \u003cimg src = \"https://user-images.githubusercontent.com/43280278/149261908-8bce72cf-b343-4bf8-8462-8be363616cfa.png\" width=\"700\"\u003e \u003c/td\u003e\n    \u003ctd\u003e \u003cp align=\"top\"\u003e \u003cimg src = \"https://user-images.githubusercontent.com/43280278/149262066-7b93538a-2ccc-4ea0-9187-ef1b54734392.png\" width=\"400\"\u003e \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cp align=\"center\"\u003e\u003cb\u003eRain streak\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cp align=\"center\"\u003e\u003cb\u003eRain drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eImage Dehazing\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n\u003cimg src = \"https://user-images.githubusercontent.com/43280278/149261947-22954827-ce62-44e8-974a-0aa8d94a4bd9.png\"  width=\"250\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eImage Enhancement\u003c/strong\u003e (click to expand) \u003c/summary\u003e\n\n\u003cimg src = \"https://user-images.githubusercontent.com/43280278/149262540-77d16592-9305-4fd7-80c6-b9d30000cc29.png\" width=\"400\"\u003e\n\u003c/details\u003e\n\n## Citation\nShould you find this repository useful, please consider citing:\n```\n@article{tu2022maxim,\n  title={MAXIM: Multi-Axis MLP for Image Processing},\n  author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},\n  journal={CVPR},\n  year={2022},\n}\n```\n\n## Acknowledgement\n\nThis repository is built on the [vision_transformer](https://github.com/google-research/vision_transformer) and [musiq](https://github.com/google-research/google-research/tree/master/musiq) repositories. Our work is also inspired by [HiT](https://github.com/google-research/hit-gan), [MPRNet](https://github.com/swz30/MPRNet), and [HINet](https://github.com/megvii-model/HINet).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle-research%2Fmaxim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoogle-research%2Fmaxim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle-research%2Fmaxim/lists"}