Single Image Interpolation Using Texture‐Aware Low‐Rank Regularization. Issue 2 (1st March 2018)

• Record Type: Journal Article
• Authors: Gao, Zhirong; Ding, Lixin; Xiong, Chengyi
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 2(2018)
• Page Start: 374
• Page End: 380
• Publication Date: 2018-03-01
• Subjects: Image interpolation -- Nonlocal selfsimilarity -- Adaptive low rank approximation -- Texture information -- Weighted partial singular values thresholding; approximation theory -- entropy -- image denoising -- image reconstruction -- image resolution -- image texture -- interpolation -- iterative methods -- matrix algebra -- splines (mathematics); texture‐aware low‐rank regularization -- image priors -- nonlocal self‐similarity -- traditional cubic‐spline interpolation -- nonlocal similar image patches -- reconstruction model -- texture information -- adaptive low rank approximation -- image quality -- initial high resolution image -- PSVT method -- Split Bregman iteration algorithm -- HR image -- data matrix entropy -- SBI algorithm -- weighted partial singular values thresholding method -- single image interpolation method -- peak signal to noise ratio -- PSNR -- structural similarity -- SSIM; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2017.08.025
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : A new image interpolation method is proposed by using the image priors of nonlocal self‐similarity and low rank approximation. Here the traditional cubicspline interpolation is conducted to obtain an initial High resolution (HR) image. The nonlocal similar image patches are vectorized to form data matrices with low rank prior, and thus a low rank regularization term is embedded into the reconstruction model. The texture information measured by entropy of the data matrix is extracted and used to achieve adaptive low rank approximation for retaining the latent fine details of image. The Split bregman iteration (SBI) algorithm and weighted Partial singular values thresholding (PSVT) method are adopted to obtain the optimum solution of the reconstruction model. Experimental results demonstrate the effectiveness of the proposed method in improving image quality in terms of Peak signal to noise ratio (PSNR) and/or Structural similarity (SSIM).