Two-Stage Low-light Image Enhancement Based on Wavelet Transform

In low-light environments, images often suffer from insufficient brightness, decreased contrast, and blurred details, leading to significant degradation of visual quality. To address these issues, this paper proposes a dual-stage wavelet transform-based method for low-light image enhancement. The method builds upon wavelet transform theory and employs a U-Net architecture to progressively achieve feature encoding, decoding, and enhancement through two sequential stages: preliminary restoration and fine-grained enhancement. For effective noise suppression and detail enhancement, we design an enhanced wavelet-domain feature fusion module that integrates discrete wavelet transform, inverse discrete wavelet transform, and dual attention mechanisms. Meanwhile, the proposed dynamic gated spatial attention and lightweight fusion-curve attention mechanisms collaborate within this feature fusion module to process image features with refined adaptability. Additionally, a fusion perceptual loss function is developed to guide the model in generating visually natural enhanced images with authentic details by jointly optimizing pixel-level errors and perceptual quality metrics. Experimental results demonstrate that our method achieves state-of-the-art performance on key metrics (e.g., PSNR, SSIM) across multiple public low-light datasets, exhibiting superior capabilities in both noise suppression and detail recovery.

搜集汇总

背景与挑战

背景概述

该数据集提出了一种基于小波变换的两阶段低光图像增强方法，旨在解决低光环境下图像亮度不足、对比度下降和细节模糊的问题。方法采用U-Net架构和双重注意力机制，通过初步恢复和精细增强阶段实现噪声抑制与细节恢复，并在多个公共数据集上验证了其在PSNR和SSIM指标上的先进性能。