Image Restoration and Construction

Bates, Russell; McDonnell, Michael A.; Christiansen, W. A.; DeGraff, David

doi:10.1063/1.4822766

Cited by 11 publications

(23 citation statements)

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“…This is a common problem in optics (digital image processing) known as the deconvolution problem whose solution is fundamental to image restoration and reconstruction [4], [5]. In terms of the material presented in this paper, there are two Point Spread Functions (PSF) p(x, y) that have been considered: For full diffusion (strong scattering)…”

Section: Deconvolutionmentioning

confidence: 99%

“…with additive noise) leading to the classification of different 'inverse filters' (e.g. [4], [5]). If a priori information on the statistics of the noise function n and the object function I 0 is available, then Bayesian estimation methods are preferable in the design of filters whose performance then depends on statistical parameters such as the standard deviation.…”

Section: Deconvolutionmentioning

confidence: 99%

“…While this approach provides physically informative models for the PDF that can be used for the statistical characterisation of an image and image segmentation (using a moving 'window') to locate statistically significant features, it does not directly help in the development of algorithms for image restoration and reconstruction [4]. On the other hand, random walk models provide the physical basis for diffusion processes in general.…”

Section: Introductionmentioning

confidence: 99%

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Imaging reconstruction for light scattering from a tenuous random medium

Blackledge¹

2009

IET Irish Signals and Systems Conference (ISSC 2009)

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Section: Deconvolutionmentioning

confidence: 99%

Section: Deconvolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Imaging reconstruction for light scattering from a tenuous random medium

Blackledge¹

2009

IET Irish Signals and Systems Conference (ISSC 2009)

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“…The study of the determination of a function, either continuous or discrete, from its amplitude spectrum has a long history, see for example, Akutowicz [8,9], Barakat and Newsam [5], and Van Hove, Lim, and Oppenheim [6]. Many of the studies also discuss the possible recovery of the function from its amplitude spectrum, see for example, Hayes, Lim, and Oppenheim [10], Hayes [11], Taylor [7], and Bates and McDonnell [12].…”

Section: Introductionmentioning

confidence: 99%

Discrete Fourier transform based pattern classifiers

Hui¹,

Żak²

2014

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract.A technique for pattern classification using the Fourier transform combined with the nearest neighbor classifier is proposed. The multidimensional fast Fourier transform (FFT) is applied to the patterns in the data base. Then the magnitudes of the Fourier coefficients are sorted in descending order and the first P coefficients with largest magnitudes are selected, where P is a design parameter. These coefficients are then used in further processing rather than the original patterns. When a noisy pattern is presented for classification, the pattern's P Fourier coefficients with largest magnitude are extracted. The coefficients are arranged in a vector in the descending order of their magnitudes. The obtained vector is referred to as the signature vector of the corresponding pattern. Then the distance between the signature vector of the pattern to be classified and the signature vectors of the patterns in the data base are computed and the pattern to be classified is matched with a pattern in the data base whose signature vector is closest to the signature vector of the pattern being classified.

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“…Great attention has been paid to solving the phase problem in optics, particularly to tackling the problems of diagnosing the structure of objects in microscopy, astrophysics and biomedicine [1][2][3][4]. The phase problem involves restoring spatial (coordinate) phase distribution in complex fields of the speckle-field type [5,6].…”

Section: Introductionmentioning

confidence: 99%