Yin Mohan, Yang Jianbai
School of Computer Science and Information Engineering, Harbin Normal University, Harbin, Heilongjiang, China.
PLoS One. 2025 Feb 13;20(2):e0314541. doi: 10.1371/journal.pone.0314541. eCollection 2025.
Images captured in nighttime or low-light environments are often affected by external factors such as noise and lighting. Aiming at the existing image enhancement algorithms tend to overly focus on increasing brightness, while neglecting the enhancement of color and detailed features. This paper proposes a low-light image enhancement network based on a combination of iterative learning mechanisms and Retinex theory (defined as ILR-Net) to enhance both detail and color features simultaneously. Specifically, the network continuously learns local and global features of low-light images across different dimensions and receptive fields to achieve a clear and convergent illumination estimation. Meanwhile, the denoising process is applied to the reflection component after Retinex decomposition to enhance the image's rich color features. Finally, the enhanced image is obtained by concatenating the features along the channel dimension. In the adaptive learning sub-network, a dilated convolution module, U-Net feature extraction module, and adaptive iterative learning module are designed. These modules respectively expand the network's receptive field to capture multi-dimensional features, extract the overall and edge details of the image, and adaptively enhance features at different stages of convergence. The Retinex decomposition sub-network focuses on denoising the reflection component before and after decomposition to obtain a low-noise, clear reflection component. Additionally, an efficient feature extraction module-global feature attention is designed to address the problem of feature loss. Experiments were conducted on six common datasets and in real-world environments. The proposed method achieved PSNR and SSIM values of 23.7624dB and 0.8653 on the LOL dataset, and 26.8252dB and 0.7784 on the LOLv2-Real dataset, demonstrating significant advantages over other algorithms.
在夜间或低光照环境下拍摄的图像通常会受到噪声和光照等外部因素的影响。针对现有的图像增强算法往往过于注重提高亮度，而忽略了颜色和细节特征的增强。本文提出了一种基于迭代学习机制和Retinex理论相结合的低光照图像增强网络（定义为ILR-Net），以同时增强细节和颜色特征。具体来说，该网络在不同维度和感受野上持续学习低光照图像的局部和全局特征，以实现清晰且收敛的光照估计。同时，在Retinex分解后的反射分量上应用去噪过程，以增强图像丰富的颜色特征。最后，通过沿通道维度连接特征来获得增强后的图像。在自适应学习子网络中，设计了扩张卷积模块、U-Net特征提取模块和自适应迭代学习模块。这些模块分别扩展网络的感受野以捕获多维度特征，提取图像的整体和边缘细节，并在收敛的不同阶段自适应地增强特征。Retinex分解子网络专注于对分解前后的反射分量进行去噪，以获得低噪声、清晰的反射分量。此外，还设计了一个高效的特征提取模块——全局特征注意力，以解决特征丢失的问题。在六个常见数据集和实际环境中进行了实验。所提出的方法在LOL数据集上的PSNR和SSIM值分别为23.7624dB和0.8653，在LOLv2-Real数据集上为26.8252dB和0.7784，表明相对于其他算法具有显著优势。
