Super-Resolution Based on Fast Registration and Maximum a Posteriori Reconstruction

Abstract

In this paper, we propose a maximum a posteriori ramework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low-resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework.

Super-resolution: a comprehensive survey

“…The above affine photometric model can only handle small photometric changes, therefore it has been extended to a non-linear model in [289], which also [113], [115], [116], [122], [128], [133], [138], [147], [149], [170], [174], [177], [181], [186], [187], [188], [189], [199], [204], [209], [215], [216], [217], [218], [223], [224], [226], [227], [228], [230], [231], [235], [242], [246], [248], [249], [250], [251], [252], [258], [260], [270], [272], [276], [277], [279], [280], …”

Image Super-resolution with Improved Wiener Restoration and Simultaneous Edge Enhancement

“…Approach considering blur as a cue has also been proposed by Rajan et al [18]. A wavelet based super resolution approach was proposed by H Ji and C Fermuller [19].…”

Variational Bayesian Image Restoration Based on a Product of $t$-Distributions Image Prior

“…The main theoretical contribution is that by constraining the approximation of the posterior in the variational framework, we bypass the need for knowing the normalization constant of this prior. Thus, we avoid having to use improper priors, i.e., priors whose normalization constant is empirically selected; see, for example, [9]- [11], [17], [18], and [19]. Furthermore, the proposed methodology does not require empirical parameter selection as in the MAP methodology that uses a similar-in-spirit prior in [17] and [18].…”

“…In [17], [18], and [19], some of us proposed a new hierarchical image prior for image restoration, image super-resolution, and blind image deconvolution problems, respectively. This prior is Student-t based, is in a product form, and is able to capture the local image discontinuities and thus provide edge-preserving capabilities for those problems.…”