A general sampling theory for nonideal acquisition devices

Unser, Michaël; Aldroubi, Akram

doi:10.1109/78.330352

Cited by 328 publications

(395 citation statements)

References 20 publications

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“…This is because our problem represented by equation (1) is an illposed inverse problem [19][20][21][22]. The treatment of ill-posed inverse problem in the presence of noise is performed using different techniques such as regularization techniques and Wiener filtering techniques [18]. In this paper, we present an efficient regularized solution to the problem of image interpolation.…”

Section: Lr Image Degradation Modelmentioning

confidence: 99%

“…This correction filter is obtained as the inverse of the cross correlation sequence between the acquisition model filter and the reconstruction filter [18]. Unfortunately, the estimation of this correction filter in our case is difficult or even impossible.…”

Section: Lr Image Degradation Modelmentioning

confidence: 99%

“…The target of the image interpolation process is the estimation of the vector f given the vector g. According to the modern sampling theory, this process requires a correction pre-filtering step in the reconstruction process [18]. This correction filter is obtained as the inverse of the cross correlation sequence between the acquisition model filter and the reconstruction filter [18].…”

Section: Lr Image Degradation Modelmentioning

confidence: 99%

“…This approach is based on the optimization of the image interpolation formula using a single controlling parameter to preserve edges through the interpolation process. Better results are expected if the pre-requisites of the modern sampling theory are considered in the interpolation process [18].…”

Section: Introductionmentioning

confidence: 99%

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A new approach for regularized image interpolation

et al. 2005

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Section: Lr Image Degradation Modelmentioning

confidence: 99%

Section: Lr Image Degradation Modelmentioning

confidence: 99%

Section: Lr Image Degradation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new approach for regularized image interpolation

et al. 2005

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“…To this extent we apply a reconstruction method similar to the method proposed by Janssen et al [16,17,18,19]. The case of stationary reconstruction is discussed in Section 3.…”

Section: Introductionmentioning

confidence: 99%

Optic Flow from Multi-scale Dynamic Anchor Point Attributes

Janssen

Florack

Duits

et al. 2006

Lecture Notes in Computer Science

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Abstract. Optic flow describes the apparent motion that is present in an image sequence. We show the feasibility of obtaining optic flow from dynamic properties of a sparse set of multi-scale anchor points. Singular points of a Gaussian scale space image are identified as feasible anchor point candidates and analytical expressions describing their dynamic properties are presented. Advantages of approaching the optic flow estimation problem using these anchor points are that (i) in these points the notorious aperture problem does not manifest itself, (ii) it combines the strengths of variational and multi-scale methods, (iii) optic flow definition becomes independent of image resolution, (iv) computations of the components of the optic flow field are decoupled and that (v) the feature set inducing the optic flow field is very sparse (typically < 1 2 % of the number of pixels in a frame). A dense optic flow vector field is obtained through projection into a Sobolev space defined by and consistent with the dynamic constraints in the anchor points. As opposed to classical optic flow estimation schemes the proposed method accounts for an explicit scale component of the vector field, which encodes some dynamic differential flow property.

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Digital Control

Yamämoto

Nagahara

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article gives a brief account on digital control, particularly sampled‐data control where signals are sampled with a uniform rate. We first review basic notions such as sampling and hold, digital‐to‐analog (DA) converter, z‐transform, and pulse transfer functions. The relationship of sampling theorem with aliasing and controllability is also reviewed. We then discuss standard classical design methodologies and problems inherent to them. The problem lies in the fact that sampled‐data systems operate on two time sets ‐ continuous and discrete. This is usually the cause of difficulties encountered in the classical design methods, in particular the oscillatory intersample behavior called ripples. This problem is resolved by introducing a new technique called lifting. It is seen that lifting makes linear continuous‐time time‐invariant plant an infinite‐dimensional discrete‐time system, thereby making the total system a time‐invariant discrete‐time system. Time‐invariant notions, such as steady‐state response, frequency response, transfer functions, can then be fully recovered. This framework makes it possible to formulate and solve robust control such as H2 and H‐infinity control and improve upon the continuous‐time performance dramatically. The same design philosophy is then applied to digital signal processing, where one can use this robust control method to optimize the intersample signals optimally. Robust sampled‐data control is suitably applied to the signal processing context, and is seen to optimally reproduce intersample high‐frequency signals. Applications to digital signal processing are discussed. The article concludes with reviews on the existing literature and recent advances in sampled‐data control as well as discussing some future directions.

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A general sampling theory for nonideal acquisition devices

Cited by 328 publications

References 20 publications

A new approach for regularized image interpolation

A new approach for regularized image interpolation

Optic Flow from Multi-scale Dynamic Anchor Point Attributes

Digital Control

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