Coarsetofine Double Linear Iteration Method for Generating Superpixels To improve the performance of the superpixel segmentation algorithm and extract the edge information of the input image, a coarsetofine double linear iteration (CDLI) method for generating superpixels is proposed in this article. CDLI can be seen as an improvement of the SLIC (simple linear iterative cluster). In order to improve the accuracy of the superpixel segmentation algorithm, the density of the input image should be uneven. Regions with sparce edge information should have lower superpixel density while regions with rich edge information should have higher superpixel density. In order to achieve this goal, CDLI coarsely partitions the input image firstly, with a method which is similar to the SLIC. On the basis of rough segmentation, the region that needs further fine segmentation is selected. Finally, the selected region is partitioned with higher precision to form the final result. The entire process is equivalent to running SLIC twice. In a series of experiments, it shows that CDLI significantly improves the segmentation accuracy of the SLIC by nearly 1 percentage point while slightly reducing the time efficiency of the SLIC.

Halftone Image Reconstruction Based on SLIC Superpixel Algorithm. This paper proposes a halftone image reconstruction based on the SLIC (Simple Linear Iterative Clustering) superpixel algorithm and the affinity propagation algorithm. Firstly, the halftone image is segmented based on SLIC superpixel algorithm. Secondly, the affinity propagation algorithm is used to clustering the regions segmented by superpixel Algorithm. After deleting the background, the image is vectorized. The smooth background image is obtained by the linear smoothing filter and nonlinear smoothing filters. Finally, the vectored boundary and smooth background are combined together to get the reconstructed image. The boundary information is effectively retained during the reconstruction. The proposed method can effectively remove the halftone patterns and screen patterns.