Locally adaptive regularized super-resolution on video with arbitrary motion

Lee, Ivan; Bose, N.K.; Lin, Chia-Hsien

doi:10.1109/icip.2010.5653819

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Given a set of LR observations, in the first SR algorithms of this group (Table 6) the following simple steps were involved: first, one of the LR images was chosen as a reference image and the others were registered against it (e.g., by optical flow [58], [115], [116], [190], [299], [468]), then the reference image is scaled up by a specific scaling factor and the other LR images were warped into that using the registration information. Then, the HR image is generated by fusing all the images together and finally an optional deblurring kernel may be applied to the result.…”

Section: Direct Methodsmentioning

confidence: 99%

“…- [318], [426], [460], [464], [468] they are defined based on steering kernel regression which takes into account the correlation between the pixel positions and their values. - [518] they are found using Zernike moments.…”

Section: Direct Methodsmentioning

confidence: 99%

“…For example, in [10] [113], [217], [224], [293], [295], [315], [328], [331], [429], [432], [468], [512], [529], [551] a MAP method has been used for simultaneous reconstruction of the super-resolved image and registration of the LR images. In these cases, the MAP formulation of Eq.…”

Section: Simultaneous Methodsmentioning

confidence: 99%

“…Therefore, these small motions are first found in the smaller images and then are interpolated in the lower level (higher resolution) images of the pyramid until the original image is met. This method, known as optical flow, works quite well when motion is to be computed between objects, which are non-rigid, non-planar, non-Lambertian, and are subject to self occlusion, like human faces, [55], [57], [58], [71], [82], [99], [100], [115], [116], [128], [126], [176], [190], [270], [288], [295], [296], [299], [307], [414], [468], [478], [492], [500], [529], [534], [555], [592]. It is discussed in [592] that using optical flows of strong candidate feature points (like those obtained by Scale Invariant Feature Transform (SIFT)) for SR algorithms produces better results than dense optical flows in which the flow involves every pixel.…”

Section: Geometric Registrationmentioning

confidence: 99%

See 3 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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