Sparse Representation based Image Interpolation with Nonlocal Autoregressive Modeling

View Researcher's Other Codes

MATLAB code for the paper: “Sparse Representation based Image Interpolation with Nonlocal Autoregressive Modeling”.

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Weisheng Dong, Lei Zhang, Rastislav Lukac, and Guangming Shi
Journal/Conference Name IEEE Transactions on Image Processing
Paper Category
Paper Abstract Sparse representation has proven to be a promising approach to image super-resolution, where the low resolution (LR) image is usually modeled as the down-sampled version of its high resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such case, however, the conventional sparse representation models (SRM) become less effective because the data fidelity term will fail to constrain the image local structures. In natural images, fortunately, the many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrated that the proposed NARM based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in term of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
Date of publication 2013
Code Programming Language MATLAB

Copyright Researcher 2022