bread

Maintainer: mingcv

Total Score

16

Last updated 5/17/2024
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Paper LinkView on Arxiv

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Model overview

Bread is a low-light image enhancement model developed by researchers Xiaojie Guo and Qiming Hu. It aims to improve the quality of images with poor or irregular illumination and reduce annoying noise. Compared to similar models like stable-diffusion, gfpgan, night-enhancement, and rembg-enhance, Bread focuses specifically on low-light image enhancement rather than general image generation or manipulation.

Model inputs and outputs

Bread takes an input image and allows users to adjust two parameters: gamma correction and denoising strength. The gamma correction adjusts the overall illumination of the image, while the denoising strength controls the amount of noise reduction applied.

Inputs

  • Image: The input image to be enhanced
  • Gamma: A value between 0 and 1.5 that controls the gamma correction applied to the image
  • Strength: A value between 0 and 0.2 that controls the denoising strength

Outputs

  • Output: The enhanced image with improved illumination and reduced noise

Capabilities

Bread is capable of significantly improving the quality of low-light images by adjusting the illumination and reducing noise. It can handle a variety of low-light scenarios, including images with poor overall lighting, irregular lighting patterns, and high noise levels.

What can I use it for?

The Bread model can be useful for enhancing low-light photos and images, such as those taken in dim indoor settings, at night, or with limited lighting conditions. This can be particularly helpful for improving the quality of images for social media, photography portfolios, or other visual content where high-quality images are important. Additionally, the model could be integrated into photo editing software or mobile applications to provide automated low-light enhancement capabilities.

Things to try

One interesting aspect of Bread is its ability to handle different types of low-light conditions. Try experimenting with the gamma correction and denoising strength parameters to see how they affect the output for a variety of low-light input images. Additionally, you could try using Bread in combination with other image enhancement or editing techniques to further improve the quality of your low-light photos.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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