用语义信息增强图像拼接，让前景物体更连贯。
SemanticStitch: Enhancing Image Coherence through Foreground-Aware Seam Carving
- 引入语义先验，让拼接时前景不被破坏
- 新损失函数提升显著，大幅改善拼接质量
- 适合需要高质量拼接的摄影与遥感应用
图像拼接常因拍摄角度差异、位置偏移和物体运动导致错位与视觉不一致。传统缝合算法忽略语义信息，造成前景连续性中断。我们提出SemanticStitch，一种基于深度学习的框架，通过融入前景对象的语义先验，保护其完整性并提升视觉一致性。方法包含一种强调显著对象语义完整性的新型损失函数，实验表明该方法在多个指标上显著优于传统技术。我们还构建了两个专门用于评估真实场景下拼接效果的大型数据集。结果验证了该方法在实际应用中的鲁棒性与有效性。
原文摘要 · Abstract (English)
Image stitching often faces challenges due to varying capture angles, positional differences, and object movements, leading to misalignments and visual discrepancies. Traditional seam carving methods neglect semantic information, causing disruptions in foreground continuity. We introduce SemanticStitch, a deep learning-based framework that incorporates semantic priors of foreground objects to preserve their integrity and enhance visual coherence. Our approach includes a novel loss function that emphasizes the semantic integrity of salient objects, significantly improving stitching quality. We also present two specialized real-world datasets to evaluate our method's effectiveness. Experimental results demonstrate substantial improvements over traditional techniques, providing robust support for practical applications.
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