maxim s2 enhancement lol

Introduction

The MAXIM model is pre-trained for image enhancement tasks and is detailed in the paper "MAXIM: Multi-Axis MLP for Image Processing." This model is capable of executing various image processing tasks, including deblurring, deraining, denoising, dehazing, low-light image enhancement, and retouching.

Architecture

MAXIM utilizes a shared MLP-based backbone to address different image processing challenges. The architecture is designed to enhance images effectively through multiple axes processing.

Training

The original authors did not release training code for MAXIM. For comprehensive details on the training process and methodologies, refer to the original paper. The model achieves a PSNR of 23.43 and an SSIM of 0.863, as noted in the research documentation.

Guide: Running Locally

To use the MAXIM model for image enhancement, follow these steps:

1. Install necessary libraries:

   pip install tensorflow pillow numpy huggingface_hub
   

2. Load and prepare the image:

   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))
   

3. Load the model and make predictions:

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

For improved performance, it is recommended to use cloud GPUs, such as those offered by Google Cloud or AWS.

License

The MAXIM model is released under the Apache 2.0 License, permitting use, distribution, and modification under the terms specified in the license agreement.