Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark

TL;DR

This paper introduces DCI-Net, a novel stereo image enhancement model for dark conditions that effectively improves detail recovery and illumination adjustment by decoupling cross-scale and cross-view interactions.

Contribution

The paper proposes a decoupled interaction module and a spatial-channel information mining block to enhance intra-view and inter-view feature extraction in low-light stereo images.

Findings

Achieves state-of-the-art performance on multiple datasets.

Improves detail recovery and illumination adjustment.

Demonstrates superior cross-scale and cross-view feature interaction.

Abstract

Low-light stereo image enhancement (LLSIE) is a relatively new task to enhance the quality of visually unpleasant stereo images captured in dark condition. However, current methods achieve inferior performance on detail recovery and illumination adjustment. We find it is because: 1) the insufficient single-scale inter-view interaction makes the cross-view cues unable to be fully exploited; 2) lacking long-range dependency leads to the inability to deal with the spatial long-range effects caused by illumination degradation. To alleviate such limitations, we propose a LLSIE model termed Decoupled Cross-scale Cross-view Interaction Network (DCI-Net). Specifically, we present a decoupled interaction module (DIM) that aims for sufficient dual-view information interaction. DIM decouples the dual-view information exchange into discovering multi-scale cross-view correlations and further exploring cross-scale information flow. Besides, we present a spatial-channel information mining block (SIMB) for intra-view feature extraction, and the benefits are twofold. One is the long-range dependency capture to build spatial long-range relationship, and the other is expanded channel information refinement that enhances information flow in channel dimension. Extensive experiments on Flickr1024, KITTI 2012, KITTI 2015 and Middlebury datasets show that our method obtains better illumination adjustment and detail recovery, and achieves SOTA performance compared to other related methods. Our codes, datasets and models will be publicly available.