Video Super-Resolution Using Generalized Gaussian Markov Random Fields

Chen, Jin; Nunez-Yanez, Jose; Achim, Alin

doi:10.1109/lsp.2011.2178595

Cited by 37 publications

(18 citation statements)

References 14 publications

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“…Therefore, these small motions are first found in the smaller images and then are interpolated in the lower level (higher resolution) images of the pyramid until the original image is met. This method, known as optical flow, works quite well when motion is to be computed between objects, which are non-rigid, non-planar, non-Lambertian, and are subject to self occlusion, like human faces, [55], [57], [58], [71], [82], [99], [100], [115], [116], [128], [126], [176], [190], [270], [288], [295], [296], [299], [307], [414], [468], [478], [492], [500], [529], [534], [555], [592]. It is discussed in [592] that using optical flows of strong candidate feature points (like those obtained by Scale Invariant Feature Transform (SIFT)) for SR algorithms produces better results than dense optical flows in which the flow involves every pixel.…”

Section: Geometric Registrationmentioning

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%

“…- [441], [463], [526], [555] the same process has been applied to all the three RGB channels of the images. - [117] the information of every two different channels of an RGB image are combined to improve the third one, then at the end, the three improved channels are combined to produce a new improved color image.…”

Section: Color Imagesmentioning

confidence: 99%

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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: Geometric Registrationmentioning

confidence: 99%

Section: Markov Random Fields (Mrf)mentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

292

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“…Superresolution (SR) reconstruction technology [1][2][3][4] aims to reconstruct high-resolution (HR) video sequences from their lowresolution (LR) counterparts. With rapid and significant development of computer vision, there is a growing need for HR videos.…”

Section: Introductionmentioning

confidence: 99%

“…SR techniques have been developed to solve SR problems from the frequency domain to the spatial domain. Currently relevant studies include three main categories: interpolation-based SR methods [5,6], multiframe-based SR methods [7][8][9], and learning-based SR methods [10,11]. Interpolation-based SR methods have relatively low computational cost and therefore are well suited for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Video Superresolution Reconstruction Using Spatiotemporal Nonlocal Similarity

Liang

2015

Mathematical Problems in Engineering

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Aiming at improving the video visual resolution quality and details clarity, a novel learning-based video superresolution reconstruction algorithm using spatiotemporal nonlocal similarity is proposed in this paper. Objective high-resolution (HR) estimations of low-resolution (LR) video frames can be obtained by learning LR-HR correlation mapping and fusing spatiotemporal nonlocal similarities between video frames. With the objective of improving algorithm efficiency while guaranteeing superresolution quality, a novel visual saliency-based LR-HR correlation mapping strategy between LR and HR patches is proposed based on semicoupled dictionary learning. Moreover, aiming at improving performance and efficiency of spatiotemporal similarity matching and fusion, an improved spatiotemporal nonlocal fuzzy registration scheme is established using the similarity weighting strategy based on pseudo-Zernike moment feature similarity and structural similarity, and the self-adaptive regional correlation evaluation strategy. The proposed spatiotemporal fuzzy registration scheme does not rely on accurate estimation of subpixel motion, and therefore it can be adapted to complex motion patterns and is robust to noise and rotation. Experimental results demonstrate that the proposed algorithm achieves competitive superresolution quality compared to other state-of-the-art algorithms in terms of both subjective and objective evaluations.

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Integrating the Missing Information Estimation into Multi-frame Super-Resolution

Chen

Liang

Zhao

et al. 2015

Circuits Syst Signal Process

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Multi-frame super-resolution image reconstruction aims to restore a highresolution image by fusing a set of low-resolution images. The low-resolution images are usually subject to some degradation, such as warping, blurring, down-sampling, or noising, which causes substantial information loss in the low-resolution images, especially in the texture regions. The missing information is not well estimated using existing traditional methods. In this paper, having analyzed the observation model describing the degradation process starting with a high-resolution image and moving to the low-resolution images, we propose a more reasonable observation model that integrates the missing information into the super-resolution reconstruction. Our approach is fully formulated in a Bayesian framework using the Kullback-Leibler divergence. In this way, the missing information is estimated simultaneously with the B Shengrong Zhao Circuits Syst Signal Process high-resolution image, motion parameters, and hyper-parameters. Our proposed estimation of the missing information improves the quality of the reconstructed image. Experimental results presented in this paper show improved performance compared with that of existing traditional methods.

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Video Super-Resolution Using Generalized Gaussian Markov Random Fields

Cited by 37 publications

References 14 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Learning-Based Video Superresolution Reconstruction Using Spatiotemporal Nonlocal Similarity

Integrating the Missing Information Estimation into Multi-frame Super-Resolution

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