SDI-Net: Toward Sufficient Dual-View Interaction for Low-light Stereo Image Enhancement

TL;DR

SDI-Net introduces a dual-view interaction model for low-light stereo image enhancement, leveraging cross-view correlations with an attention-based module to improve enhancement quality over existing methods.

Contribution

The paper proposes SDI-Net, a novel dual-view interaction network with a Cross-View Sufficient Interaction Module for better exploitation of stereo view correlations.

Findings

Outperforms existing methods on public datasets.

Shows significant improvement in image quality metrics.

Ablation studies confirm the effectiveness of key modules.

Abstract

Currently, most low-light image enhancement methods only consider information from a single view, neglecting the correlation between cross-view information. Therefore, the enhancement results produced by these methods are often unsatisfactory. In this context, there have been efforts to develop methods specifically for low-light stereo image enhancement. These methods take into account the cross-view disparities and enable interaction between the left and right views, leading to improved performance. However, these methods still do not fully exploit the interaction between left and right view information. To address this issue, we propose a model called Toward Sufficient Dual-View Interaction for Low-light Stereo Image Enhancement (SDI-Net). The backbone structure of SDI-Net is two encoder-decoder pairs, which are used to learn the mapping function from low-light images to normal-light images. Among the encoders and the decoders, we design a module named Cross-View Sufficient Interaction Module (CSIM), aiming to fully exploit the correlations between the binocular views via the attention mechanism. The quantitative and visual results on public datasets validate the superiority of our method over other related methods. Ablation studies also demonstrate the effectiveness of the key elements in our model.