Blind superresolution

Filip Šroubek (), Gabriel Cristóbal () and Jan Flusser
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Filip Šroubek: Academy of Sciences of the Czech Republic, ÚTIA
Gabriel Cristóbal: CSIC, Instituto de Óptica
Jan Flusser: Academy of Sciences of the Czech Republic, ÚTIA

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 133-145 from Springer

Abstract: Abstract This paper presents a unifying approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Experiments on real data illustrate the robustness and utilization of the proposed technique.

Keywords: Blind deconvolution; superresolution; multiframe image restoration; MIMO (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_11

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DOI: 10.1007/978-3-7908-1709-6_11

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