A Robust and Computationally Efficient Simultaneous Super-Resolution Scheme for Image Sequences

Zibetti, Marcelo V. W.; Mayer, Joceli

doi:10.1109/tcsvt.2007.903801

Cited by 38 publications

(35 citation statements)

References 42 publications

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“…For example, in [10] [113], [217], [224], [293], [295], [315], [328], [331], [429], [432], [468], [512], [529], [551] a MAP method has been used for simultaneous reconstruction of the super-resolved image and registration of the LR images. In these cases, the MAP formulation of Eq.…”

Section: Simultaneous Methodsmentioning

confidence: 99%

“…In [606] the TV terms are weighted with an adaptive spatial algorithm based on differences in the curvature. Table 8), which is used to approximate TV, is defined by: [123], [277], [328], [363], [365], [368], [388], [414], [500], [547], [551], [ [123], [133], [149], [253], [260], [272], [276], [282], [313], [314], [328], [375], [428], [452], [588] GMRF [123], [129], [163], [181], [209], [276], [282], [305], [496], [535], [555], [574], [575], [598] TV [73], [84], [124], [157], [245], …”

Section: Markov Random Fields (Mrf)mentioning

confidence: 99%

See 1 more Smart Citation

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

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: Simultaneous Methodsmentioning

confidence: 99%

Section: Markov Random Fields (Mrf)mentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

292

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“…The existing SR video approaches can be classified into the following four categories: (i) the sliding-window-based SR video approach [93][94][95][96], (ii) the simultaneous SR video approach [97][98][99], (iii) the sequential SR video approach [100][101][102][103][104], and (iv) the learning-based SR video approach [105][106][107]. In the following, a review of each category is presented.…”

Section: Super-resolution Video Reconstructionmentioning

confidence: 99%

“…Many works ( [97][98][99], for examples) have been done to simultaneously reconstruct multiple high-resolution images at one time, as illustrated in Fig. 8.…”

Section: Simultaneous Sr Video Approachmentioning

confidence: 99%

A survey on super-resolution imaging

Tian

2011

SIViP

254

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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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“…So, subjective visual comparison of the images provides information about the spatial distortion and artifacts introduced by an algorithm employed, and present the performance of the analyzed technique when the SR image or SR frame of the video sequence are observed by the human visual system. The motion estimation is one of the fundamental problems in the treatment of the digital video sequences (Wüst Zibetti et al 2007;Callico et al, 2008;Kravchenko et al, 2009). The objective of motion estimation consists of calculating the field of motion vectors to describe the apparent movement between two images of the sequence.…”

Section: Performance Criteriamentioning

confidence: 99%