Single‐frame image super‐resolution using learned wavelet coefficients

Jiji, C. V.; Joshi, Manjunath V.; Chaudhuri, Subhasis

doi:10.1002/ima.20013

Cited by 79 publications

(68 citation statements)

References 32 publications

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“…From the obtained results, we can see that the proposed method also realizes more successful reconstruction of the HR images than those of the previously reported methods. As shown in Figures 4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22, the difference between the proposed method and the previously reported methods becomes more significant as the amount of the high-frequency components in the target images becomes larger. In detail, regions at sculptures and characters, respectively, shown in Figures 21 and 22 have successfully been reconstructed by the proposed method.…”

Section: Ogawa and Haseyamamentioning

confidence: 81%

“…On the other hand, in the learning-based approach, the HR image is recovered by utilizing several other images as training data. These motion-free techniques have been adopted by many researchers, and a number of learning-based SR methods have been proposed [9][10][11][12][13][14][15][16][17][18]. For example, Freeman et al proposed example-based SR methods that estimate missing high-frequency components from mid-frequency components of a target image based on Markov networks and provide an HR image [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive example-based super-resolution using kernel PCA with a novel classification approach

Ogawa

Haseyama

2011

EURASIP J. Adv. Signal Process.

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An adaptive example-based super-resolution (SR) using kernel principal component analysis (PCA) with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution (LR) images, the proposed method performs clustering of high-resolution (HR) patches clipped from training HR images in advance. Based on two nonlinear eigenspaces, respectively, generated from HR patches and their corresponding low-frequency components in each cluster, an inverse map, which can estimate missing high-frequency components from only the known lowfrequency components, is derived. Furthermore, by monitoring errors caused in the above estimation process, the proposed method enables adaptive selection of the optimal cluster for each target local patch, and this corresponds to the novel classification approach in our method. Then, by combining the above two approaches, the proposed method can adaptively estimate the missing high-frequency components, and successful reconstruction of the HR image is realized.

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Section: Ogawa and Haseyamamentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Adaptive example-based super-resolution using kernel PCA with a novel classification approach

Ogawa

Haseyama

2011

EURASIP J. Adv. Signal Process.

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“…Similar methods based on the DWT have been developed for SR in [143] (2003), [150], [179], [257], [459]. In [162], [302], [320], [345], [399], [436], [447], [476], [549], [564], [565], but the results obtained by DWT are used as a regularization term in Maximum a Posteriori (MAP) formulation of the problem (Section 5.1.6). In [390], [423] they have been used with Compressive Sensing (CS) methods (Section 5.2.1) and in [425] within a PCA-based face hallucination algorithm (Section 5.2).…”

Section: Wavelet Transformmentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

572

288

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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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“…In [19] we have proposed a single frame super-resolution algorithm using a waveletbased learning technique where the HR edge primitives are learned from the HR data set locally. An eigen face-domain super-resolution reconstruction algorithm for face recognition is proposed in [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Super Resolution Imaging Applied to Scientific Images

Chaudhuri¹

2007

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE JUL 20072. REPORT TYPE The final version of the code is being delivered along with this report. Also, the latest code and the results of the feasibility studies are being sent to the AFRL/ML collaborators. Dr. Seetharaman also visited IIT Bombay during April 16-30, 2007 on a related research mission and had an opportunity to verify and be trained on using the developed computer programs.The contract was to investigate the feasibility of super resolution imaging of large surfaces at nano-scale resolution. Four different techniques were considered for this study, and the computational results indicate a promising performance with a peak PSNR gain of 6 db over standard bilinear interpolation. In some cases, even a scale factor of 4 produced reliable results. Specifically the effort included: (1) a thorough survey of the state of the art, (2) data collected by US collaborators and analyzed by the investigator, (3) development of Papoulis-Gerchberg method to implement the analytic continuation of spectral details, (4) exploration of contourlet and its variant known as the Laplacian Edge model. Additionally, we developed a new technique for super resolution of material images known as total variation optimized image interpolation. The deliverance of this report serves to meet the fifth objective of the original contract.Place: Mumbai

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Single‐frame image super‐resolution using learned wavelet coefficients

Cited by 79 publications

References 32 publications

Adaptive example-based super-resolution using kernel PCA with a novel classification approach

Adaptive example-based super-resolution using kernel PCA with a novel classification approach

Super-resolution: a comprehensive survey

Super Resolution Imaging Applied to Scientific Images

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