Dynamic Grouped Interaction Network for Low-Light Stereo Image Enhancement

Low-Light Stereo Image Enhancement (LLSIE) tackles the challenge of improving the illumination and restoring the details in stereo images. However, existing deep learning-based LLSIE methods trained on high-resolution low-light images often exhibit sub-optimal performance when interacting with information from the left and right views. We find that this is because of: (1) the high computational cost arising from quadratic complexity, which hinders the enhancement model's ability to process high-resolution images; and (2) the limitations of conventional fusion strategies in previous work, which inadequately capture cross-view cues, resulting in weak feature representation and compromised detail recovery. To address these limitations, we propose a novel Dynamic Grouped Interaction Network (DGI-Net) to enhance illumination and recover more details while reducing the computational cost. Specifically, DGI-Net employs the U-Net structure, which effectively mitigates noise during the low-light enhancement. Furthermore, we design a Grouped Stereo Interaction Module (GSIM) with a grouping strategy to efficiently discover cross-view cues while minimizing computations. To dynamically fuse stereo information and fully exploit cross-view correlations, we also introduce a Dynamic Embedding Module (DEM) to establish dynamic connections between inter-view cues and intra-view features, which performs dynamic weight processing on cross-view cues to eliminate noise during fusion. For intra-view processing, we present a Diversity Enhanced Block (DEB) to extract multi-scale features, thereby improving diversity and feature representation. This multi-scale feature extraction also addresses low image contrast in dark lighting conditions. Experimental results demonstrate that DGI-Net outperforms current state-of-the-art methods in low-light stereo image enhancement.