JigsawNet: Shredded Image Reassembly Using Convolutional Neural Network and Loop-Based Composition
This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge is to reliably compute correct pairwise matching, for which most existing algorithms use handcrafted features, and cannot reliably handle complicated puzzles. We build a deep convolutional neural network (CNN) to detect the compatibility of pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, instead of using the widely adopted greedy edge selection strategies, we propose two new loop closure-based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially challenging ones with many fragment pieces.
Publication Source (Journal or Book title)
IEEE Transactions on Image Processing
Le, C., & Li, X. (2019). JigsawNet: Shredded Image Reassembly Using Convolutional Neural Network and Loop-Based Composition. IEEE Transactions on Image Processing, 28 (8), 4000-4015. https://doi.org/10.1109/TIP.2019.2903298