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Statistics for image sharpening
Statistica Neerlandica, 2007Sharpening filters increase the depth of digital images by adding a fraction of their gradient. This portion is tuned by a coefficient which is usually selected according to rules of thumb or subjective evaluation. This paper proposes statistical measures for designing such a parameter in a nearly automatic way, avoiding subjective evaluations.
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Statistical analysis of PET images
ACM SIGHIT Record, 2012In recent decades, neuroimaging techniques have become relevant and essential supports to the diagnosis and therapy of neurological diseases. Positron Emission Tomography (PET) is a method of functional investigation that measures brain metabolism and identifies the changes that occur at cellular and molecular level, supporting physicians for studying ...
Patrizia Vizza +2 more
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Statistical recognition of color images
Applied Optics, 1987The feasibility of classification of stochastic images for color vision in real time has been investigated with two approaches. First, a hybrid incoherent optical correlator based on a quasi-monochromatic cathode ray tube (CRT) is sequentially operated on red, green, and blue channels for statistical pattern recognition.
Z H, Gu, S H, Lee, Y, Fainman
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Statistical-Based Image Tagging
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2016The automation of image tagging is extremely important research topic in recent years due to its importance in building large image databases. The optimal goal of recent research is to automatically annotate images and overcome the semantic gap between the image content and the associated text representation. Image retrieval from large databases is one
Mohamed Eid Mahmoud Masoud +2 more
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Fuzzy statistics of digital images
IEEE Signal Processing Letters, 1996The notion of first- and second-order fuzzy statistics of digital images is presented. Owing to the inherent imprecision in the gray values, fuzzy statistics have been observed to behave better in representing the spatial gray distribution in a digital image.
C. V. Jawahar, A. K. Ray
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Statistics of the contrast of coherent images
Journal of the Optical Society of America A, 2004Contrast optimization, also known as image sharpening, is a method that can be used to estimate phase errors in coherent images. However, the contrast measure of a coherent image is a random variable because of the speckle present in coherent images. The variance of this measure puts a limit on the ability of contrast optimization to focus an image. We
S A, Fortune, M P, Hayes, P T, Gough
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Statistics of natural image distortions
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Natural scene statistics (NSS) are an active area of research. Although there exist elegant models for NSS, the statistics of natural image distortions have received little attention. In this paper we study distorted image statistics (DIS) for natural scenes.
Anush K. Moorthy, Alan C. Bovik
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Statistics of natural and urban images
1997We investigated ensembles of artificial and real-world grey-scale images to find different invariance properties: translation invariance, scale invariance and a new hierarchical invariance recently proposed by Ruderman [1]. We found that the assumption of translational invariance can be taken for granted. Our results concerning the scale invariance are
Christian Ziegaus, Elmar Wolfgang Lang
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Statistical imaging and complexity regularization
IEEE Transactions on Information Theory, 2000Summary: We apply the complexity-regularization principle to statistical ill-posed inverse problems in imaging. The class of problems studied includes restoration of images corrupted by Gaussian or Poisson noise and nonlinear transformations. We formulate a natural distortion measure in image space and develop nonasymptotic bounds on estimation ...
Pierre Moulin, Juan Liu
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On the Natural Statistics of Chromatic Images
2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2018The visual brain is optimally designed to process images from the natural environment that we perceive. Describing the natural environment statistically helps in understanding how the brain encodes those images efficiently. The Natural Scene Statistics (NSS) of the luminance component of images is the basis of several univariate and bivariate ...
Zeina Sinno, Alan C. Bovik
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