H.5. Image Processing and Computer Vision
Kimia Peyvandi
Abstract
Image inpainting is one of the important topics in the field of image processing, and various methods have been proposed in this area. However, this problem still faces multiple challenges, as an inpainting algorithm may perform well for a specific class of images but may have poor performance for other ...
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Image inpainting is one of the important topics in the field of image processing, and various methods have been proposed in this area. However, this problem still faces multiple challenges, as an inpainting algorithm may perform well for a specific class of images but may have poor performance for other images. In this paper, we attempt to decompose the image into a low-rank component and a sparse component using (Principal Component Analysis) PCA, and then independently restore each component. For inpainting the low-rank component, we use an algorithm based on low-rank minimization, and for restoring the sparse component, we use the concept of splines. Using splines, we can effectively restore edges and lines, whereas the restoration of these regions is challenging in most algorithms. Also, in restoring the low-rank component, we construct a tensor at each step and approximate the missing pixels in the tensor, thereby significantly improving the efficiency of the low-rank minimization idea in image inpainting. Finally, we have applied our proposed method to restore various types of images, which demonstrates the effectiveness of our proposed method compared to other inpainting methods based on PSNR and SSIM.
