Analysis of multiframe super-resolution reconstruction for image anti-aliasing and deblurring

Wang, Zhaozhong; Qi, Feihu

doi:10.1016/j.imavis.2004.11.001

Cited by 19 publications

(13 citation statements)

References 30 publications

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Section: Imaging Modelsmentioning

confidence: 99%

“…(4)), some researchers have considered reversing the order by first applying the blurring and then the warping [171], [212], [234]. It is discussed in [171] and [212] that the former coincides more with the general imaging physics (where the camera blur is dominant), but it may result in systematic errors if motion is being estimated from the LR images [171], [214]. However, some other researchers have mentioned that these two operations can commute and be assumed as block-circulate matrices, if the point spread function is space invariant, normalized, has nonnegative elements, and the motion between the LR images is translational [156], [157], [158], [186], [588], [610].…”

Section: Imaging Modelsmentioning

confidence: 99%

“…It is discussed in [212] that one may check the practical limits of reconstruction based SR algorithms which are regularized by Tikhonov-like terms by Discrete Picard Condition (DPC).…”

Section: K+1mentioning

confidence: 99%

See 2 more Smart Citations

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Section: Imaging Modelsmentioning

confidence: 99%

Section: Imaging Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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“…The limitation of SR computation mainly comes from the following factors [24][25][26][27][28][29]: interpolation error, quantization error, motion estimation error and optical-blur. Baker and Kanade [30] showed that, for a sufficiently large resolution enhancement factor, any smoothness prior image model will result in reconstructions with very little high-frequency content.…”

Section: Introductionmentioning

confidence: 99%

A survey on super-resolution imaging

Tian

2011

SIViP

260

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The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as 'low-resolution' images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of SR image and video reconstruction methods developed in the literature and highlight the future research challenges. The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images, and the SR video approaches reconstruct an image sequence with a higher-resolution from a group of adjacent lower-resolution image frames. Furthermore, several SR applications are discussed to contribute some insightful comments on future SR research directions. Specifically, the SR computations for multi-view images and the SR video computation in the temporal domain are discussed.

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“…The effect of regularization is considered in [22], and the performance of RBAs is analyzed through the discrete Picard condition (DPC). The DPC is used to indicate the performance limits when the M is selected and the image motion is estimated.…”

Section: Introductionmentioning

confidence: 99%