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license: apache-2.0
library_name: keras
language: en
- vision
- maxim
- image-to-image
- gopro
# MAXIM pre-trained on GoPro for image deblurring
MAXIM model pre-trained for image deblurring. 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 32.86 and an SSIM of 0.961.
## Intended uses & limitations
You can use the raw model for image deblurring 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 = ""
image =, 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-deblurring-gopro")
predictions = model.predict(tf.expand_dims(image, 0))
For a more elaborate prediction pipeline, refer to [this Colab Notebook](
### Citation
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},