Quality-Aware Seam-Cutting and Content-Preserving Warping for Parallax Image Stitching

Parallax images often suffer from insufficient and unstable point features, making accurate alignment in overlapping regions extremely challenging. To address this issue, this paper proposes a robust image stitching framework based on content-preserving warping and quality-aware seam-cutting. The framework first estimates a global homography by combining point and line features to achieve reliable initial alignment. Subsequently, an energy function composed of alignment, distortion, and saliency terms is optimized to reduce geometric deformation while preserving image structures. After obtaining an initial seam using conventional seam-cutting, a structural similarity (SSIM)-based quality evaluation mechanism is introduced to identify misaligned regions and extract corresponding patches. To refine local alignment, the scale-invariant feature transform flow (SIFT flow) is employed to estimate dense correspondences, and a sigmoid-based smoothing constraint is adopted to avoid boundary artifacts. Finally, the optimized patches are integrated into the final result using a revised seam-cutting strategy with enhanced data penalty terms. Experimental results demonstrate that the proposed method achieves competitive performance, with favorable artifact suppression and structural preservation compared with mainstream stitching methods.