Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
This addresses a real-world issue in reconstructing broken artifacts or ruined frescoes, but it is an incremental improvement combining existing techniques for a specific domain.
The paper tackles the problem of solving jigsaw puzzles with missing or eroded borders by proposing JiGAN, a two-step method that first repairs borders using a GAN-based image extension and then solves the puzzle with relaxation labeling, demonstrating feasibility on benchmark datasets.
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.