Use of Motion Information in Super-Resolution Mosaicing

Krämer, Petra; Hadar, Ofer; Benois-Pineau, Jenny; Domenger, Jean‐Philippe

doi:10.1109/icip.2006.313167

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The above affine photometric model can only handle small photometric changes, therefore it has been extended to a non-linear model in [289], which also [113], [115], [116], [122], [128], [133], [138], [147], [149], [170], [174], [177], [181], [186], [187], [188], [189], [199], [204], [209], [215], [216], [217], [218], [223], [224], [226], [227], [228], [230], [231], [235], [242], [246], [248], [249], [250], [251], [252], [258], [260], [270], [272], [276], [277], [279], [280], …”

Section: Imaging Modelsmentioning

confidence: 99%

“…If the imaging device is not available, but only a set of LR images are, it can be estimated by techniques known as Blind Deconvolution [275] in which the blur can be estimated by degradation of features like small points or sharp edges or by techniques like Generalized Cross Validation (GCV) [68], [92]. In this group, the blur can be estimated globally (space-invariant blurring) [5] (1987), [6], [8], [9], [13], [14]) or locally (space-variant blurring The blurring caused by motion depends on the direction of the motion, the velocity, and the exposure time [242], [273], [542]. It is shown in [542] that tempo-ral blur induces temporal aliasing and can be exploited to improve the SR of moving objects in video sequences.…”

Section: Blur Estimationmentioning

confidence: 99%

“…Using a high-pass filter for deblurring in the context of SR was first proposed by Keren et al [6]. In Tekalp et al [16] and then in [58], [87], [242], [463], [531] a Wiener filter and in [128] an Elliptical Weighted Area (EWA) filter has been used for this purpose.…”

Section: Blur Estimationmentioning

confidence: 99%

“…Beside increasing the speed of the algorithm it has been shown that this increases the robustness of the algorithm against the outliers which can be generated by different sources of errors, such as errors in the motion estimation [124]. Table 5 Reported IBP works [5], [6], [8], [13], [14] [124], [125], [128], [138], [147], [172], [177], [242], [249], [264], [270], [279], [280] [284], [325], [369], [392], [406], [446], [492], [501], [539], [546], [552] …”

Section: Iterative Back Projectionmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

586

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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