Reticular shield detection algorithm using superpixel segmentation

In this paper, we propose a novel reticular shield detection algorithm to restore the images that are obscured by reticular shield such as barbed wires and fences. The whole framework is composed of three stages: stage-1 for accurate superpixel segmentation, stage-2 for deriving the mask of reticular shield and stage-3 for restoring the image by introducing the algorithm. In this paper, to obtain the accurate mask of the shield, a new joint feature CCTP is used to train support vector machine (SVM) classifier and classify all superpixels. Finally, we make comparison of the effects of the proposed algorithm and several other algorithms put forward by other scientists. We introduce two indexes, PSNR and SSIM to evaluate the results and the proposed algorithm is obviously superior to the other two algorithms, which can also be proved by looking into the restoration images.