MAXIM pre-trained on FiveK for image retouching

MAXIM model pre-trained for image retouching. It was introduced in the paper MAXIM: Multi-Axis MLP for Image Processing by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released in this repository .

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.

Model description

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:

Training procedure and results

The authors didn't release the training code. For more details on how the model was trained, refer to the original paper .

As per the table , the model achieves a PSNR of 26.15 and an SSIM of 0.945.

Intended uses & limitations

You can use the raw model for image retouching tasks.

The model is officially released in JAX . It was ported to TensorFlow in this repository .

How to use

Here is how to use this model:

from huggingface_hub import from_pretrained_keras
from PIL import Image

import tensorflow as tf
import numpy as np
import requests

url = "https://github.com/sayakpaul/maxim-tf/raw/main/images/Enhancement/input/748.png"
image = Image.open(requests.get(url, stream=True).raw)
image = np.array(image)
image = tf.convert_to_tensor(image)
image = tf.image.resize(image, (256, 256))

model = from_pretrained_keras("google/maxim-s2-enhancement-fivek")
predictions = model.predict(tf.expand_dims(image, 0))

For a more elaborate prediction pipeline, refer to this Colab Notebook .

Citation

@article{tu2022maxim,
  title={MAXIM: Multi-Axis MLP for Image Processing},
  author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
  journal={CVPR},
  year={2022},
}