Modeling and extrapolation of prior scene information for set theoretic restoration and super-resolution of diffraction-limited images

Bhattacharjee, Supratik; Sundareshan, Malur K.

doi:10.1109/icip.2000.899385

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Previous survey papers on SR algorithms have mostly considered these factors as well. [5], [6], [7], [8], [13], [14], [20] [49], [50], [51], [53], [55], [56], [57], [58], [60], [61], [62], [63], [64], [65], [66], [71], [72], [73], [74], [75], [76], [80], [81], [82] [99], [100], [102], [105], [107], [108], [109], [111], [112], [113], [115], [116], [117], [118], [121], [122], [123], [124], [125], …”

Section: Taxonomy Of Sr Algorithmsmentioning

confidence: 99%

“…Another group of iterative methods are those based on the concept of POCS [7] [49], [53], [72], [77], [93], [103], [122], [155], [175], [184], [204], [243], [351]. These algorithms define an implicit cost function for solving the SR problem [155].…”

Section: Projection Onto Convex Setsmentioning

confidence: 99%

“…Different a priori knowledge have been used in the literature along with the POCS method [7] (1989), [38], [53], [72], [77], [93], [175], [184], [204], for example:…”

Section: Projection Onto Convex Setsmentioning

confidence: 99%

See 2 more Smart Citations

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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