论文标题
放松标签会遇到甘斯：解决拼宾难题与缺少边界
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
论文作者
论文摘要
本文提出了一种基于GAN的方法Jigan，该方法用于解决具有侵蚀或缺失边界的拼图难题。例如，缺少边界是一种常见的现实情况，例如，在处理破碎的伪像或破坏的壁画时。在这种特殊条件下，由于边界的差距，难题的作品不能完全对齐。在这种情况下，由于缺乏颜色和线条的连续性，补丁的直接匹配是不可行的。吉根（Jigan）是一个两个步骤的程序，可以解决此问题：首先，我们使用基于GAN的图像扩展模型来修复受侵蚀的边界，并测量零件之间的对齐亲和力；然后，我们使用放松标记算法解决难题，以在零件定位中执行一致性，从而重建拼图。我们在一个大难题的大数据集和三个常用基准数据集上测试了该方法，以证明所提出方法的可行性。
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or ruined frescoes. In this particular condition, the puzzle's pieces do not align perfectly due to the borders' gaps; in this situation, the patches' direct match is unfeasible due to the lack of color and line continuations. JiGAN, is a two-steps procedure that tackles this issue: first, we repair the eroded borders with a GAN-based image extension model and measure the alignment affinity between pieces; then, we solve the puzzle with the relaxation labeling algorithm to enforce consistency in pieces positioning, hence, reconstructing the puzzle. We test the method on a large dataset of small puzzles and on three commonly used benchmark datasets to demonstrate the feasibility of the proposed approach.
