Superresolution restoration of an image sequence: adaptive filtering approach

Elad, Michael; Feuer, Arie

doi:10.1109/83.748893

Cited by 206 publications

(186 citation statements)

References 14 publications

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“…They are usually based on various (scene or camera) motion or scanning strategy. Some of them account for possible rotation (Elad & Feuer 1999) and/or a magnifying factor (Rochefort et al 2006). Other approaches introduce an edge-preserving prior (Nguyen et al 2001;Woods et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Super-resolution in map-making based on a physical instrument model and regularized inversion

Orieux

Giovannelli

Rodet

et al. 2012

A&A

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We investigate super-resolution methods for image reconstruction from data provided by a family of scanning instruments like the Herschel observatory. To do this, we constructed a model of the instrument that faithfully reflects the physical reality, accurately taking the acquisition process into account to explain the data in a reliable manner. The inversion, i.e. the image reconstruction process, is based on a linear approach resulting from a quadratic regularized criterion and numerical optimization tools. The application concerns the reconstruction of maps for the SPIRE instrument of the Herschel observatory. The numerical evaluation uses simulated and real data to compare the standard tool (coaddition) and the proposed method. The inversion approach is capable to restore spatial frequencies over a bandwidth four times that possible with coaddition and thus to correctly show details invisible on standard maps. The approach is also applied to real data with significant improvement in spatial resolution.

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Section: Introductionmentioning

confidence: 99%

Super-resolution in map-making based on a physical instrument model and regularized inversion

Orieux

Giovannelli

Rodet

et al. 2012

A&A

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“…Then in [8] they updated their work by using the Keren registration method [9] instead of estimating and updating the registration parameters at the iterations. Elad and Feuer [10] introduced using LS cost function with L2-norm for reconstructing of continuous image sequences from the view point of adaptive filtering theory. The cost function here is a function of time and the assumed correlation between the continuous movie frames is applied to simplify the equations.…”

Section: Overview Of Dsr Techniquesmentioning

confidence: 99%

Super Resolution results in PANOPTES, an adaptive multi-aperture folded architecture

Faramarzi

Bhakta

Rajan

et al. 2010

2010 IEEE International Conference on Image Processing

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We present experimental results of digital super resolution (DSR) techniques on low resolution data collected using PANOPTES, a multi-aperture miniature folded imaging architecture. The flat form factor of PANOPTES architecture results in an optical system that is heavily blurred with space variant PSF which makes super resolution challenging. We also introduce a new DSR method called SRUM (Super-Resolution with Unsharpenning Mask) which can efficiently highlight edges by embedding an unsharpenning mask to the cost function. This has much better effect than just applying the mask after all iterations as a post-processing step.

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“…Since τ 0 v = m 0 * v is available, we replace m 0 v n in (20) by m 0 * v (i.e. τ 0 v) to improve the approximation.…”

Section: Analysis Of the Algorithmsmentioning

confidence: 99%

“…Iterative spatial domain methods are popular class of methods for solving the problems of resolution enhancement [2,19,20,21,24,30,34,37,38,41]. The problems are formulated as Tikhonov regularization.…”

Section: Introductionmentioning

confidence: 99%

High‐resolution image reconstruction with displacement errors: A framelet approach

Chan¹,

Riemenschneider²,

Shen³

et al. 2004

Int J Imaging Syst Tech

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High-resolution image reconstruction arises in many applications, such as remote sensing, surveillance, and medical imaging. The model of Bose and Boo [2] can be viewed as the passage of the high-resolution image through a blurring kernel built from the tensor product of a univariate low-pass filter of the form 1 2 + , 1, · · · , 1, 1 2 − , where is the displacement error. When the number L of low-resolution sensors is even, tight frame symmetric framelet filters were constructed in [8] from this low-pass filter using the unitary extension principle of [43]. The framelet filters do not depend on , and hence the resulting algorithm reduces to that of the case where = 0. Furthermore, the framelet method works for symmetric boundary conditions. This greatly simplifies the algorithm. However, both the design of the tight framelets and extension to symmetric boundary are only for even L and cannot be applied to the case when L is odd. In this paper, we design tight framelets and derive a tight framelet algorithm with symmetric boundary conditions that work for both odd and even L. An analysis of the convergence of the algorithms is also given. The details of the implementations of the algorithm are also given.

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Superresolution restoration of an image sequence: adaptive filtering approach

Cited by 206 publications

References 14 publications

Super-resolution in map-making based on a physical instrument model and regularized inversion

Super-resolution in map-making based on a physical instrument model and regularized inversion

Super Resolution results in PANOPTES, an adaptive multi-aperture folded architecture

High‐resolution image reconstruction with displacement errors: A framelet approach

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