Abstract
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: this https URL.
Abstract (translated)
低光图像会遭受复杂的退化影响，而现有的增强方法通常在一个单一的潜在空间中编码所有退化因素。这导致了高度纠缠的功能特性及强烈的黑箱特征，使得模型容易陷入捷径学习。为了缓解上述问题，本文提出了一种基于小波变换的低光照立体图像增强方法，并通过解耦特征空间来解决这些问题。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. 通过对多级小波分解提取出的低光图像中的低频分量进行调整，可以实现照明调节。 因此，利用小波变换将特征空间分解为一个用于照明调整的低频分支和多个用于纹理增强的高频分支。此外，立体低光照图像增强可以从另一个视角中提取有用的线索来改进增强效果。为此，我们提出了一种新颖的高频引导跨视图交互模块（HF-CIM），该模块在高频分支内而不是在整个特征空间上运行，有效地从另一视角中提取有价值的图像细节。 为了增强高频信息，还基于交叉注意力机制提出了一个细节和纹理增强模块（DTEM）。模型是在包含均匀照明和非均匀照明的图像数据集上进行训练的。实验证明，在真实和合成图像上的实验结果表明，我们的算法在光线调整方面具有显著优势，同时有效地恢复了高频信息。 代码和数据集公开获取链接：[此URL](this https URL)。
URL
https://arxiv.org/abs/2507.12188
