A Learning-Based Method for Image Super-Resolution From Zoomed Observations

Joshi, Manjunath V.; Chaudhuri, Subhasis; Panuganti, Rajkiran

doi:10.1109/tsmcb.2005.846647

Cited by 58 publications

(43 citation statements)

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“…- [129], [163], [244], [246], [448], [596] change the imaging model of Eq. (4) to include the effect of different zooming in the LR images.…”

Section: Imaging Modelsmentioning

confidence: 99%

“…which can be rewritten aŝ [113], [118], [123], [124], [125], [129], [163], [184], [188], [189], [216], [251], [252], [253], [312], [279], [291], [406], [450] [104], [105], [107], [108], [109], [110], [112], [123], [127], [129], [133], [134], [135], [137], [138], [140], [149], [152], [157], [160], [161], [163], [165], [170], [181], [184], [187], [199], [209], [215], [217]...…”

Section: Maximum a Posteriorimentioning

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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“…- [129], [163], [244], [246], [448], [596] change the imaging model of Eq. (4) to include the effect of different zooming in the LR images.…”

Section: Imaging Modelsmentioning

confidence: 99%

Section: Maximum a Posteriorimentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

589

292

View full text Add to dashboard Cite

show abstract

“…Joshi and Chaudhuri [18] have proposed a learning-based method for image superresolution from zoomed observations. They model the high-resolution image as a Markov random field (MRF), the parameters of which are learned from the most zoomed observation.…”

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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“…In this paper, we address the problem of multiresolution fusion from a different perspective, based on superresolution techniques [23], [24]. The idea of superresolution refers to the process of producing a high spatial resolution image from several low-resolution images, by increasing the maximum spatial frequency and removing the degradations that arise during image acquisition from a low-resolution camera.…”

Section: Introductionmentioning

confidence: 99%