Fast video super-resolution via classification

Simonyan, Karen; Grishin, S. G.; Vatolin, Dmitriy; Popov, D. I.

doi:10.1109/icip.2008.4711763

Cited by 30 publications

(16 citation statements)

References 4 publications

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“…Then, the HR image is generated by fusing all the images together and finally an optional deblurring kernel may be applied to the result. For fusing the scaled LR images, different filters can be used, such as mean and median filters [125], [156], [157], [190], [216], [299], [479], adaptive normalized averaging [167], Adaboost classifier [364], and SVD-based filters [582]. These algorithms have been shown to be much faster than the IBP algorithms [ [58], [74], [115], [116], [124], [125], [156], [157], [226] (the last five are known as shift and add), [167], [176], [190], [299], [319], [364], [397], [479], [544], [546], [582] Non-parametric [418], [419], [426], [439], [514], [560], [563], [617] In [125], [156], [157], [216] it was shown that the median fusion of the LR images when they are registered, is equivalent to the ML estimation of the residual of the imaging model of Eq.…”

Section: Direct Methodsmentioning

confidence: 99%

“…Given a reference image and a new image, block matching [55], [77], [160], [176], [210], [251], [252], [308], [309], [333], [364], [366], [369], [397], [593] divides the images into blocks of equal or adaptive sizes [366]. Each block in the reference image is then compared against every block in a neighborhood of blocks in the new image.…”

Section: Geometric Registrationmentioning

confidence: 99%

“…Each block in the reference image is then compared against every block in a neighborhood of blocks in the new image. Different search techniques are possible for finding the corresponding block of a reference block in the new image: Sum of Absolute Differences (SAD) [364], [369], [397], [542], [544], SSD [369], Sum of Absolute Transform Differences (SATD), Sum of Squared Transform Differences (SSTD) [282]. A comparison of these search techniques can be found in [333].…”

Section: Geometric Registrationmentioning

confidence: 99%

See 2 more Smart Citations

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

592

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: Direct Methodsmentioning

confidence: 99%

Section: Geometric Registrationmentioning

confidence: 99%

Section: Geometric Registrationmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

592

292

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“…1. [7] and then getting the area with student by the detection method combining skin color detection [8] and AdaBoost classifier [9] from the foreground. At last pattern recognition method, hough forests classification [10], is used to find the student's status (payable to listen, play, sleep, etc.)…”

Section: Students Status Analysis and Teaching Effect Evaluationmentioning

confidence: 99%

Students Status Analysis and Class Teaching Effect Evaluation System Based on the Analysis of Monitoring Video

Wang¹

2016

Proceedings of the 2016 International Conference on Education, Management and Computer Science

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Abstract. Students status analysis is important to the teaching effect evaluation and improvement. In this paper, we propose a method to acquire the status of students by analyzing the monitoring video processing. We use the HD cameras in front of the classroom to capture the real time class video and transmit it to the servers for processing. For students status analysis, we firstly extract the student area by foreground and background segmentation and then use the skin color detection and AdaBoost classifier to find the faces of students. At last pattern recognition method is used to find the student's status. The statistical results of status could be given to the teachers for improving their teaching effect.

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“…The reason is that image upscaling is an important part of many image and video processing algorithms, e.g. super-resolution [9], and a distinct task as well. A special attention has been paid to the problem since the emergence of High Definition Television (HDTV).…”

Section: Introductionmentioning

confidence: 99%

Edge-Directed Interpolation in a Bayesian Framework

Simonyan

Vatolin

2009

Procedings of the British Machine Vision Conference 2009

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In this paper we present a novel framework for Edge-Directed Interpolation (EDI) of still images. The problem is treated as finding maximum a posteriori estimates of each interpolated pixel type and intensity value. The pixel type may be one of the pre-defined edge directions or "non-edge". Instead of the separate steps of edge orientation detection and intensity interpolation, maximizing the joint probability density function of type and intensity provides a better fit to the local image structure. Such a technique allows an effective discrimination between edges and non-edges (uniform areas and texture), thus leading to the suppression of artifacts which are common to existing EDI methods. Objective and subjective comparisons with conventional EDI methods corroborate the advantages of the proposed one. Moreover, the locality and the low computational complexity of the method make it suitable for a hardware implementation.

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Fast video super-resolution via classification

Cited by 30 publications

References 4 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Students Status Analysis and Class Teaching Effect Evaluation System Based on the Analysis of Monitoring Video

Edge-Directed Interpolation in a Bayesian Framework

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