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Nonparametric Blind Super-Resolution Using Adaptive Heavy-Tailed Priors
NUPT, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China.;NUPT, Natl Engn Res Ctr Commun & Networking, Nanjing, Jiangsu, Peoples R China..
NUPT, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China..
NUPT, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China..
Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA.;Southeast Univ, Sch Automat, Nanjing, Jiangsu, Peoples R China..
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2019 (English)In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 61, no 6, p. 885-917Article in journal (Refereed) Published
Abstract [en]

Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 61, no 6, p. 885-917
Keywords [en]
Super-resolution, Blind deconvolution, Camera shake deblurring, Discriminative models, Convolutional neural networks, Normalized sparsity, Relative total variation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-255571DOI: 10.1007/s10851-019-00876-1ISI: 000475765100008Scopus ID: 2-s2.0-85062626918OAI: oai:DiVA.org:kth-255571DiVA, id: diva2:1340203
Note

QC 20190802

Available from: 2019-08-02 Created: 2019-08-02 Last updated: 2019-08-02Bibliographically approved

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Li, Haibo

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