Multi-scale interaction network for stereo image super-resolution

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Liyi Xu and Lin Qi introduced a novel multi-scale interaction network designed for stereo image super-resolution, a task focused on generating high-resolution images by leveraging complementary information from binocular systems. Submitted on May 21, 2026, this network addresses the underexploited potential of both intra-view and cross-view information in existing methods. The proposed architecture incorporates a Multi-scale Spatial-Channel Attention Module, which enhances intra-view feature extraction through multi-scale large separable kernel attention and simple channel attention. Additionally, it features a Dual-View Epipolar Attention Module that employs an optimal transport algorithm to achieve more precise matching along the epipolar line. Experimental and ablation studies demonstrate that this method achieves competitive results, surpassing most state-of-the-art techniques in performance.

Key takeaway

For Computer Vision Engineers developing stereo image super-resolution systems, consider integrating multi-scale attention mechanisms and optimal transport algorithms. Your current methods for intra-view feature extraction and cross-view matching could be significantly improved by adopting the Multi-scale Spatial-Channel Attention Module and Dual-View Epipolar Attention Module. This approach, demonstrated to outperform existing state-of-the-art techniques, offers a path to higher resolution outputs and more accurate binocular information utilization in your applications.

Key insights

A multi-scale interaction network enhances stereo image super-resolution by optimizing intra-view and cross-view information exploitation.

Principles

• Intra-view features benefit from multi-scale spatial-channel attention.
• Cross-view matching improves with optimal transport along epipolar lines.

Method

The network integrates a Multi-scale Spatial-Channel Attention Module for intra-view features and a Dual-View Epipolar Attention Module using optimal transport for accurate epipolar line matching.

In practice

• Implement multi-scale separable kernel attention for feature extraction.
• Apply optimal transport for precise stereo image matching.

Topics

• Stereo Image Super-Resolution
• Multi-scale Attention
• Epipolar Geometry
• Optimal Transport
• Computer Vision
• Deep Learning Networks

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.