Journal Article10.1049/IP-VIS:19990238
Estimation of image noise variance
K. Rank,M. Lendl,Rolf Unbehauen +2 more
- 01 Apr 1999
- Vol. 146, Iss: 2, pp 80-84
283
TL;DR: A novel algorithm for estimating the noise variance of an image that is assumed to be corrupted by Gaussian distributed noise and an ensemble of 128 natural and artificial test images is used to compare with several previously published estimation methods.
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Abstract: A novel algorithm for estimating the noise variance of an image is presented. The image is assumed to be corrupted by Gaussian distributed noise. The algorithm estimates the noise variance in three steps. At first the noisy image is filtered by a horizontal and a vertical difference operator to suppress the influence of the (unknown) original image. In a second step a histogram of local signal variances is computed. Finally a statistical evaluation of the histogram provides the desired estimation value. For a comparison with several previously published estimation methods an ensemble of 128 natural and artificial test images is used. It is shown that with the novel algorithm more accurate results can be achieved.
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Citations
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Use of minimal inter-quantile distance estimation in image processing
TL;DR: A new method based on using inter-quantile distance and its minimization for obtaining appropriately accurate estimates of noise variance is proposed, shown that mathematically this task can be formulated as finding a mode of contaminated asymmetric distribution and can be met for other applications.
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•Proceedings Article
Estimation based non-local approach for pre-processing of MRI
Harshit Tiwari,Vikrant Bhateja,Aditya Srivastava +2 more
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TL;DR: A patch based approach where the decomposed patches of MRI with low texture strength are selected on the basis of gradient covariance matrix are used to estimate the noise level through Principal Component Analysis (PCA) to improve the noise suppression.
7
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An Introduction to Ray tracing
Andrew Glassner
- 11 Feb 1989
TL;DR: An Introduction to Ray Tracing is an excellent reference dedicated completely to ray tracing, and presents in detail many of the design considerations one might consider when implementing a ray tracing system.