DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains

TL;DR

DLEN is a novel dual-branch Transformer-based network that uses spatial and frequency domain attention mechanisms, including a learnable wavelet transform, to significantly improve low-light image enhancement quality.

Contribution

The paper introduces DLEN, a dual-branch Transformer architecture with frequency-aware modules and attention mechanisms for superior low-light image enhancement.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively preserves details and natural appearance in enhanced images.

Utilizes wavelet transform for better edge and texture preservation.

Abstract

Low-light image enhancement (LLE) aims to improve the visual quality of images captured in poorly lit conditions, which often suffer from low brightness, low contrast, noise, and color distortions. These issues hinder the performance of computer vision tasks such as object detection, facial recognition, and autonomous driving.Traditional enhancement techniques, such as multi-scale fusion and histogram equalization, fail to preserve fine details and often struggle with maintaining the natural appearance of enhanced images under complex lighting conditions. Although the Retinex theory provides a foundation for image decomposition, it often amplifies noise, leading to suboptimal image quality. In this paper, we propose the Dual Light Enhance Network (DLEN), a novel architecture that incorporates two distinct attention mechanisms, considering both spatial and frequency domains. Our model introduces a learnable wavelet transform module in the illumination estimation phase, preserving high- and low-frequency components to enhance edge and texture details. Additionally, we design a dual-branch structure that leverages the power of the Transformer architecture to enhance both the illumination and structural components of the image.Through extensive experiments, our model outperforms state-of-the-art methods on standard benchmarks.Code is available here: https://github.com/LaLaLoXX/DLEN