Multi-scale interaction network for stereo image super-resolution

2026-05-20

AI Summary

Existing stereo image super-resolution methods struggle to effectively integrate intra-view details with inter-view complementary information. To address this challenge, this work proposes a multi-scale interaction network that enhances single-view feature representation through a multi-scale spatial-channel attention module and introduces a dual-view epipolar attention module to achieve precise cross-view matching along epipolar lines by incorporating epipolar geometric constraints with an optimal transport algorithm. The method innovatively combines large separable convolutional kernel attention with a geometry-guided optimal transport mechanism. Extensive experiments demonstrate that the proposed approach outperforms most state-of-the-art methods on multiple benchmark datasets, and ablation studies confirm the effectiveness of each component.

Abstract

Stereo image super-resolution aims to generate high-resolution images by leveraging complementary information from binocular systems. Although previous studies have achieved impressive results, the potential of intra-view and cross-view information has not been fully exploited. To address this issue, we propose a novel multi-scale interaction network for stereo image super-resolution. Specifically, we design a Multi-scale Spatial-Channel Attention Module that utilizes multi-scale large separable kernel attention and simple channel attention to improve intra-view feature extraction. Additionally, we propose a Dual-View Epipolar Attention Module, utilizing an optimal transport algorithm to achieve more accurate matching along the epipolar line. Extensive experimental and ablation studies show that our method achieves competitive results that outperform most SOTA methods.

Problem

• stereo image super-resolution
• intra-view information
• cross-view information
• epipolar line
• binocular systems

Innovation

• multi-scale interaction
• spatial-channel attention
• epipolar attention
• optimal transport
• stereo image super-resolution