标题： 基于小波的低光立体图像增强解耦框架 显示英文标题

标题： Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement

摘要： 低光图像受到复杂的退化影响，现有的增强方法通常将所有退化因素编码在一个潜在空间中。 这导致特征高度纠缠和强烈的黑箱特性，使模型容易出现捷径学习。 为缓解上述问题，本文提出了一种基于小波的低光立体图像增强方法，具有特征空间解耦。 我们的见解来自以下发现：(1) 小波变换能够独立处理低频和高频信息。 (2) 通过多级小波分解提取的低频分量可以调整光照。 因此，通过使用小波变换，特征空间被分解为用于光照调整的低频分支和用于纹理增强的多个高频分支。 此外，立体低光图像增强可以从另一个视角提取有用线索以提高增强效果。 为此，我们提出了一种新颖的高频引导跨视角交互模块（HF-CIM），该模块在高频分支内运行，而不是在整个特征空间中，从而有效从另一个视角提取有价值的图像细节。 此外，为了增强高频信息，提出了一种基于交叉注意力机制的细节和纹理增强模块（DTEM）。 该模型在一个包含均匀光照图像和非均匀光照图像的数据集上进行训练。 在真实和合成图像上的实验结果表明，我们的算法在光照调整方面具有显著优势，同时能有效恢复高频信息。 代码和数据集可在以下网址公开获取：https://github.com/Cherisherr/WDCI-Net.git. 显示英文摘要

摘要： Low-light images suffer from complex degradation, and existing enhancement methods often encode all degradation factors within a single latent space. This leads to highly entangled features and strong black-box characteristics, making the model prone to shortcut learning. To mitigate the above issues, this paper proposes a wavelet-based low-light stereo image enhancement method with feature space decoupling. Our insight comes from the following findings: (1) Wavelet transform enables the independent processing of low-frequency and high-frequency information. (2) Illumination adjustment can be achieved by adjusting the low-frequency component of a low-light image, extracted through multi-level wavelet decomposition. Thus, by using wavelet transform the feature space is decomposed into a low-frequency branch for illumination adjustment and multiple high-frequency branches for texture enhancement. Additionally, stereo low-light image enhancement can extract useful cues from another view to improve enhancement. To this end, we propose a novel high-frequency guided cross-view interaction module (HF-CIM) that operates within high-frequency branches rather than across the entire feature space, effectively extracting valuable image details from the other view. Furthermore, to enhance the high-frequency information, a detail and texture enhancement module (DTEM) is proposed based on cross-attention mechanism. The model is trained on a dataset consisting of images with uniform illumination and images with non-uniform illumination. Experimental results on both real and synthetic images indicate that our algorithm offers significant advantages in light adjustment while effectively recovering high-frequency information. The code and dataset are publicly available at: https://github.com/Cherisherr/WDCI-Net.git.