Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling

被引:318
作者
Dong, Weisheng [1 ]
Zhang, Lei [2 ]
Lukac, Rastislav [3 ]
Shi, Guangming [1 ]
机构
[1] Xidian Univ, Sch Elect Engn, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
[3] Sigma Corp, Foveon Inc, San Jose, CA 95161 USA
关键词
Image interpolation; nonlocal autoregressive model; sparse representation; super-resolution; LINEAR INVERSE PROBLEMS; SIGNAL RECOVERY; ALGORITHM; SUPERRESOLUTION; DICTIONARIES; MINIMIZATION; RESTORATION; RECONSTRUCTION; REGULARIZATION; APPROXIMATION;
D O I
10.1109/TIP.2012.2231086
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Sparse representation is 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 cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, 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 demonstrate 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 terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
引用
收藏
页码:1382 / 1394
页数:13
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