Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization

Purkait, Pulak; Chanda, Bhabatosh

doi:10.1109/tip.2012.2201492

Cited by 66 publications

(36 citation statements)

References 49 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%

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: Cost Functions and Optimization Methodsmentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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“…SR is a problem of obtaining a HR image from multiple or single LR images [10], which is an inverse problem of imaging process. In imaging process, the LR image is acquired through various imaging devices which are corrupted by noise and other degraded effect [11][12][13], and the imaging process is shown in Figure 1(a). It is worthwhile to improve the resolution of LR images in some special situations.…”

Section: Basic Framework Of Srmentioning

confidence: 99%

Superresolution Reconstruction of Electrical Equipment Incipient Fault

Wang

Guo

Chen

et al. 2018

Journal of Control Science and Engineering

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With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.

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“…We solve (1.2) with the proposed adaptive regularization (3.1) by gradient descent technique. The non-differentiability and nonlinearity are handled by appropriate shrinkage operators [4]. The detail of optimization method is not included in this paper.…”

Section: Geodesic Distance Computationmentioning

confidence: 99%

“…These are called deblurring and super-resolution (SR) respectively and are classical inverse problems in image processing. In these inverse problems, the relation between the observed image Y and the desired sharp image X can be represented as [1][2][3][4] Y = HX + η (1.1) where Y , X and η represent lexicographically ordered column vectors of the observed image, desired sharp image and the additive noise respectively. H is blurring matrix for deblurring problem.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Morphologic Regularizations for Inverse Problems

Purkait

Chanda

2013

Lecture Notes in Computer Science

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Abstract. Regularization is an well-known technique for obtaining stable solution of ill-posed inverse problems. In this paper we establish a key relationship among the regularization methods with edge-preserving noise filtering method which leads to an efficient adaptive regularization methods. We show experimentally the efficiency and superiority of the proposed regularization methods for some inverse problems, e.g. deblurring and super-resolution (SR) image reconstruction.

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Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization

Cited by 66 publications

References 49 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Superresolution Reconstruction of Electrical Equipment Incipient Fault

Adaptive Morphologic Regularizations for Inverse Problems

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