|
--- |
|
license: apache-2.0 |
|
library_name: keras |
|
language: en |
|
tags: |
|
- vision |
|
- maxim |
|
- image-to-image |
|
datasets: |
|
- sidd |
|
--- |
|
|
|
# MAXIM pre-trained on SIDD for image denoising |
|
|
|
MAXIM model pre-trained for image denoising. 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). |
|
|
|
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](https://arxiv.org/abs/2201.02973). |
|
|
|
As per the [table](https://github.com/google-research/maxim#results-and-pre-trained-models), the model achieves a PSNR of 39.96 and an SSIM of 0.96. |
|
|
|
## Intended uses & limitations |
|
|
|
You can use the raw model for image denoising tasks. |
|
|
|
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). |
|
|
|
### How to use |
|
|
|
Here is how to use this model: |
|
|
|
```python |
|
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/Denoising/input/0011_23.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-s3-denoising-sidd") |
|
predictions = model.predict(tf.expand_dims(image, 0)) |
|
``` |
|
|
|
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). |
|
|
|
### Citation |
|
|
|
```bibtex |
|
@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}, |
|
} |
|
``` |
