Unlike single image task, stereo image enhancement can use another view information, and its key stage is how to perform cross-view feature interaction to extract useful information from another view. However, complex noise in low-light image and its impact on subsequent feature encoding and interaction are ignored by the existing methods. In this paper, a method is proposed to perform enhancement and de-noising simultaneously. First, to reduce unwanted noise interference, a low-frequency information enhanced module (IEM) is proposed to suppress noise and produce a new image space. Additionally, a cross-channel and spatial context information mining module (CSM) is proposed to encode long-range spatial dependencies and to enhance inter-channel feature interaction. Relying on CSM, an encoder-decoder structure is constructed, incorporating cross-view and cross-scale feature interactions to perform enhancement in the new image space. Finally, the network is trained with the constraints of both spatial and frequency domain losses. Extensive experiments on both synthesized and real datasets show that our method obtains better detail recovery and noise removal compared with state-of-the-art methods. In addition, a real stereo image enhancement dataset is captured with stereo camera ZED2. The code and dataset are publicly available at: https://www.github.com/noportraits/LFENet.

翻译：与单图像任务不同，立体图像增强可利用另一视角的信息，其关键阶段在于如何进行跨视角特征交互以从另一视角提取有用信息。然而，现有方法忽视了低光图像中的复杂噪声及其对后续特征编码与交互的影响。本文提出了一种同步实现增强与去噪的方法。首先，为减少不必要的噪声干扰，提出低频信息增强模块（IEM）抑制噪声并生成新的图像空间。其次，提出跨通道与空间上下文信息挖掘模块（CSM），用于编码长距离空间依赖关系并增强通道间特征交互。基于CSM构建编码器-解码器结构，融合跨视角与跨尺度特征交互，在新的图像空间中进行增强。最后，网络通过空间域与频率域损失的双重约束进行训练。在合成与真实数据集上的大量实验表明，与传统方法相比，本方法能更好地恢复细节并去除噪声。此外，使用立体相机ZED2采集了真实立体图像增强数据集。代码与数据集已公开于：https://www.github.com/noportraits/LFENet。