Sparse Representation Based Image Editing Technology

With the popularity of the camera, image has become an importantmedia for expressing and transferring information.Accordingly, images with higher and higher quality are required as well as the demand of images. Because of the unsuitable time or situation, the imagesmay be required to add some key persons or itemswhich did not appear on the shot, or to remove some unexpected objects from it.For the two kinds of image editing problems, this paper gives aneffective solution respectively.Adding items into the image is an importantimage composition problem. The classic solution is Poisson fusion which establishes an optimization equation to expect the results to interpolate the boundary of the composition region in target image and preserve the gradient field of source image patch.This method is suitable for the case that the offsetsof pixel at source image patch's boundary are almost the same.If there isdistinctlydifferent, it must cause the image deformation which would lead to the composition object appear blurring.Considering the fact that the edges of image are sparsely distributed compared with the whole image, their changes would not influence the object's details from the vision. Base on that, this paper proposes an image composition technology based on L0 gradient preserving. It spreadsthe image deformation caused by the offset differences of the boundarypixelsto the pixels with large gradients, so that the smallgradients which stand for the image detailwill keep unchanged. This method optimizes the image composition problem iteratively. In each step, only large gradients are updated and small gradients are always set their initial values in the source image patch. The optimization is able toweakenthe blurringphenomenoneffectively.Obstacle removal is an ill posed problem because it cannot infer the content behind the obstacle from only one image. This paper tries to solve the problem on removal of moving foreground obstacle.The input is several images taken at different moments, so themoving foreground obstacle is located at different locations of the background, and the content behind the obstacle of each image can be found at another images in image sequence.The occlusion region of the moving object is only occupied a small part of the whole image, in the meanwhile, each occlusion only appears in one or at most two images.For that reason, this paper treats this problem as a sparse problemwherethe foregroundobstacle is treated as noiseand the noise will be removed from background bylow-rank decomposing method.First of all, an alignment pre-processing will be applied to the input images sequence,and thena large matrix is constructed by treating each image as a column vector. If there is no the foreground obstacle, each column of the big matrix should be same and the big matrix must be a low-rank matrix.So the low-rank decomposition will be adopted to decompose the matrix into a low-rank background matrix and a sparseforeground matrix.