On Ambiguities in Super-Resolution Modeling

Wang, Zhaozhong; Qi, Feihu

doi:10.1109/lsp.2004.831674

Cited by 33 publications

(18 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

Section: Imaging Modelsmentioning

confidence: 99%

“…(4)), some researchers have considered reversing the order by first applying the blurring and then the warping [171], [212], [234]. It is discussed in [171] and [212] that the former coincides more with the general imaging physics (where the camera blur is dominant), but it may result in systematic errors if motion is being estimated from the LR images [171], [214]. However, some other researchers have mentioned that these two operations can commute and be assumed as block-circulate matrices, if the point spread function is space invariant, normalized, has nonnegative elements, and the motion between the LR images is translational [156], [157], [158], [186], [588], [610].…”

Section: Imaging Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

View full text Add to dashboard Cite

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.

show abstract

Section: Imaging Modelsmentioning

confidence: 99%

Section: Imaging Modelsmentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

View full text Add to dashboard Cite

show abstract

“…, y (ρ) . It is important to note that there is another observation model commonly used in the literature (e.g., [34][35][36][37]). The only difference is that the order of warping and blurring operations is reversed; that is,…”

Section: Observation Model For Super-resolution Imagementioning

confidence: 99%

A survey on super-resolution imaging

Tian

2011

SIViP

260

View full text Add to dashboard Cite

The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as 'low-resolution' images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of SR image and video reconstruction methods developed in the literature and highlight the future research challenges. The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images, and the SR video approaches reconstruct an image sequence with a higher-resolution from a group of adjacent lower-resolution image frames. Furthermore, several SR applications are discussed to contribute some insightful comments on future SR research directions. Specifically, the SR computations for multi-view images and the SR video computation in the temporal domain are discussed.

show abstract

“…where G i , which is generally different from F i [31], denotes the warping operator for the blurred image Bf . There are algorithms, adopting a fusion-deblur [5] or an interpolationrestoration [13] scheme for SRR; these algorithms are also based on the blurring-warping imaging process in (2).…”

Section: Model Analysis: From Complex To Simplementioning

confidence: 99%

“…There are two important reasons for such a choice: First model (2) describes the real imaging process for the case when the dominant blur is either the atmospheric turbulence or the motion blur as in satellite imaging or aerial photography [13]. Second Wang and Qi [31] have shown that, even if the real imaging process is the warping-blurring (1) (in the case when the camera blur is dominant), model (2) is still preferable when the motion has to be estimated from the LR images.…”

Section: The Separable Srr Modelmentioning

confidence: 99%

Analysis of multiframe super-resolution reconstruction for image anti-aliasing and deblurring

Wang

2005

Image and Vision Computing

View full text Add to dashboard Cite

On Ambiguities in Super-Resolution Modeling

Cited by 33 publications

References 18 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

A survey on super-resolution imaging

Analysis of multiframe super-resolution reconstruction for image anti-aliasing and deblurring

Contact Info

Product

Resources

About