Super Resolution of Images of 3D Scenecs

Mudenagudi, Uma; Gupta, Ankit; Goel, Lakshya; Kushal, Avanish; Kalra, Prem Kumar; Banerjee, Subhashis

doi:10.1007/978-3-540-76390-1_9

Cited by 20 publications

(15 citation statements)

References 9 publications

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“…If it is nonconvex, the time consuming simulated annealing can be used [5] (1987), [6], [135], [241], [483], or else Graduated Non-Convexity [95], [293], [496] (with normalized convolution for obtaining an initial good approximation), [540], EM [113], [181], [288], [454], Genetic Algorithm [174], Markov Chain Monte Carlo using Gibbs Sampler [209], [214], [234], [241], [254], [612], Energy Minimization using Graph-Cuts [248], [279], [305], [535], Bregman Iteration [353], [590], proximal iteration [357], (Regularized) Orthogonal Matching Pursuit [390], [464], and Particle Swarm Optimization [448] might be used. [109], [118], [133], [172], [181], [184], [197], [199], [204], [216], [218], [221], [223], [226], [229], [251]…”

Section: Cost Functions and Optimization 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

589

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: Cost Functions and Optimization Methodsmentioning

confidence: 99%

Section: Markov Random Fields (Mrf)mentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

589

292

View full text Add to dashboard Cite

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“…This technology has been an active area of research [1], [2], [3], [4], [5] because it can provide realistic 3-D visual experiences by enabling users to select viewpoints freely and interactively.…”

Section: Introductionmentioning

confidence: 99%

Super-resolved free-viewpoint image synthesis combined with sparse-representation-based super-resolution

Nakashima

Takahashi

Naemura

2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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We consider super-resolved free-viewpoint image synthesis (SR-FVS), where a high-resolution (HR) image that would be observed from a virtual viewpoint is synthesized from a set of low-resolution multi-view images. In previous studies, methods for SR-FVS were proposed on the basis of reconstruction-based super-resolution (RB-SR). RB-SR uses multiple images to synthesize an HR image and thereby can naturally be applied to SR-FVS, where multi-view images are given as the input. However, the quality of the synthesized image depends on observation conditions such as the depth of the target scene, so sometimes the quality of SR-FVS can degrade severely. To mitigate such degradation, we propose integrating learning-based super-resolution (LB-SR), which uses knowledge learned from massive natural images, into the SR-FVS process. In this paper, we adopt sparse coding super-resolution (ScSR) as a LB-SR method and combine ScSR with an existing SR-FVS method.

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“…In [1,9,14,5], the close relationship between superresolution and 3D scene structure is pointed out and their cooperative solution is studied. In [9], the super-resolution is formulated with the calibrated 3D geometry and solved using the MAP-MRF framework.…”

Section: D Reconstruction and Image Super Resolutionmentioning

confidence: 99%

“…In [9], the super-resolution is formulated with the calibrated 3D geometry and solved using the MAP-MRF framework. Occlusions are effectively handled in their super-resolution method using depth information, but super-resolution does not contribute to depth map estimation in this method.…”

Section: D Reconstruction and Image Super Resolutionmentioning

confidence: 99%

Simultaneous Super-Resolution of Depth and Images Using a Single Camera

Lee

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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In this paper, we propose a convex optimization framework for simultaneous estimation of super-resolved depth map and images from a single moving camera. The pixel measurement error in 3D reconstruction is directly related to the resolution of the images at hand. In turn, even a small measurement error can cause significant errors in reconstructing 3D scene structure or camera pose. Therefore, enhancing image resolution can be an effective solution for securing the accuracy as well as the resolution of 3D reconstruction. In the proposed method, depth map estimation and image super-resolution are formulated in a single energy minimization framework with a convex function and solved efficiently by a first-order primal-dual algorithm. Explicit inter-frame pixel correspondences are not required for our super-resolution procedure, thus we can avoid a huge computation time and obtain improved depth map in the accuracy and resolution as well as highresolution images with reasonable time. The superiority of our algorithm is demonstrated by presenting the improved depth map accuracy, image super-resolution results, and camera pose estimation.

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Super Resolution of Images of 3D Scenecs

Cited by 20 publications

References 9 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Super-resolved free-viewpoint image synthesis combined with sparse-representation-based super-resolution

Simultaneous Super-Resolution of Depth and Images Using a Single Camera

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