Summary of Ltcf-net: a Transformer-enhanced Dual-channel Fourier Framework For Low-light Image Restoration, by Gaojing Zhang and Jinglun Feng
LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration
by Gaojing Zhang, Jinglun Feng
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel network architecture, LTCF-Net, enhances low-light images by leveraging the separation of luminance from chromatic components in color images. Unlike Retinex-based methods, it uses LAB and YUV color spaces to efficiently separate and process color information, incorporating a Transformer architecture for efficient image processing. The model also introduces a Fourier transform module that adjusts the luminance channel in the frequency domain to dynamically balance brightness and eliminate background noises. Experimental results demonstrate LTCF-Net’s effectiveness in improving low-light image quality, outperforming current state-of-the-art approaches across multiple evaluation metrics and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LTCF-Net is a new way to make pictures taken in low light look better. It uses special color spaces and a special computer program called the Transformer to understand what’s in the picture. The model also has a special part that helps balance the brightness of different parts of the image, so it looks more natural. Scientists tested LTCF-Net on many images and found it works better than other ways to improve low-light pictures. |
Keywords
» Artificial intelligence » Transformer
