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--- |
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license: apache-2.0 |
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library_name: keras |
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language: en |
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tags: |
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- vision |
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- maxim |
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- image-to-image |
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datasets: |
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- lol |
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--- |
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# MAXIM pre-trained on LOL for image enhancement |
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MAXIM model pre-trained for image enhancement. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released in [this repository](https://github.com/google-research/maxim). |
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Disclaimer: The team releasing MAXIM did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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MAXIM introduces a shared MLP-based backbone for different image processing tasks such as image deblurring, deraining, denoising, dehazing, low-light image enhancement, and retouching. The following figure depicts the main components of MAXIM: |
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## Training procedure and results |
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The authors didn't release the training code. For more details on how the model was trained, refer to the [original paper](https://arxiv.org/abs/2201.02973). |
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As per the [table](https://github.com/google-research/maxim#results-and-pre-trained-models), the model achieves a PSNR of 23.43 and an SSIM of 0.863. |
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## Intended uses & limitations |
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You can use the raw model for image enhancement tasks. |
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The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). |
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### How to use |
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Here is how to use this model: |
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```python |
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from huggingface_hub import from_pretrained_keras |
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from PIL import Image |
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import tensorflow as tf |
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import numpy as np |
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import requests |
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url = https://github.com/sayakpaul/maxim-tf/raw/main/images/Dehazing/input/0048_0.9_0.2.png |
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image = Image.open(requests.get(url, stream=True).raw) |
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image = np.array(image) |
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image = tf.convert_to_tensor(image) |
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image = tf.image.resize(image, (256, 256)) |
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model = from_pretrained_keras("google/maxim-s2-enhancement-lol") |
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predictions = model.predict(tf.expand_dims(image, 0)) |
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``` |
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For a more elaborate prediction pipeline, refer to [this Colab Notebook](https://colab.research.google.com/github/sayakpaul/maxim-tf/blob/main/notebooks/inference-dynamic-resize.ipynb). |
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### Citation |
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```bibtex |
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@article{tu2022maxim, |
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title={MAXIM: Multi-Axis MLP for Image Processing}, |
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author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao}, |
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journal={CVPR}, |
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year={2022}, |
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} |
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``` |
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