LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration

TL;DR

LTCF-Net is a lightweight transformer-enhanced dual-channel Fourier framework that improves low-light image quality by leveraging color space separation, frequency domain luminance adjustment, and efficient content understanding.

Contribution

The paper introduces LTCF-Net, combining dual-color space processing, transformer architecture, and Fourier-based luminance adjustment for superior low-light image restoration.

Findings

Outperforms state-of-the-art methods on multiple datasets

Achieves more natural color restoration

Provides balanced brightness across images

Abstract

We introduce LTCF-Net, a novel network architecture designed for enhancing low-light images. Unlike Retinex-based methods, our approach utilizes two color spaces - LAB and YUV - to efficiently separate and process color information, by leveraging the separation of luminance from chromatic components in color images. In addition, our model incorporates the Transformer architecture to comprehensively understand image content while maintaining computational efficiency. To dynamically balance the brightness in output images, we also introduce a Fourier transform module that adjusts the luminance channel in the frequency domain. This mechanism could uniformly balance brightness across different regions while eliminating background noises, and thereby enhancing visual quality. By combining these innovative components, LTCF-Net effectively improves low-light image quality while keeping the model lightweight. Experimental results demonstrate that our method outperforms current state-of-the-art approaches across multiple evaluation metrics and datasets, achieving more natural color restoration and a balanced brightness distribution.