Leveraging Local Patch Alignment to Seam-cutting for Large Parallax Image Stitching

TL;DR

This paper introduces a Local Patch Alignment Module (LPAM) that improves large parallax image stitching by enhancing seam quality independently of initial alignment accuracy, leading to better mosaics.

Contribution

The novel LPAM approach integrates localized alignment into seam-cutting, addressing large parallax challenges beyond traditional alignment-dependent methods.

Findings

LPAM significantly improves stitching quality on large parallax datasets.

The method maintains computational efficiency.

Experimental results outperform existing alignment-dependent seam-cutting techniques.

Abstract

Seam cutting has shown significant effectiveness in the composition phase of image stitching, particularly for scenarios involving parallax. However, conventional implementations typically position seam-cutting as a downstream process contingent upon successful image alignment. This approach inherently assumes the existence of locally aligned regions where visually plausible seams can be established. Current alignment methods frequently fail to satisfy this prerequisite in large parallax scenarios despite considerable research efforts dedicated to improving alignment accuracy. In this paper, we propose an alignment-compensation paradigm that dissociates seam quality from initial alignment accuracy by integrating a Local Patch Alignment Module (LPAM) into the seam-cutting pipeline. Concretely, given the aligned images with an estimated initial seam, our method first identifies low-quality pixels along the seam through a seam quality assessment, then performs localized SIFT-flow alignment on the critical patches enclosing these pixels. Finally, we recomposite the aligned patches using adaptive seam-cutting and merge them into the original aligned images to generate the final mosaic. Comprehensive experiments on large parallax stitching datasets demonstrate that LPAM significantly enhances stitching quality while maintaining computational efficiency. The code is available at https://github.com/tlliao/LPAM_seam-cutting.