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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Noise modeling and estimation in image sequences from thermal infrared cameras
TL;DR: An automated procedure devised to measure noise variance and correlation from a sequence of digitized images acquired by an incoherent imaging detector is presented and it is demonstrated that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and spatially autocorrelated.
6
Patent
Method and system for estimating noise level
Zhi Yang,Alexander A. Zamyatin,Satoru Ohishi,Anusha Muthu Natarajan +3 more
- 14 Jun 2011
TL;DR: In this paper, a new concept of pseudo-standard deviation (PSD) is introduced to automatically determine simple and reliable noise level estimates, and a histogram of PSD is constructed with fine bins to calculate the moving average of the histogram.
6
Automated line flattening of Atomic Force Microscopy images
Sotirios A. Tsaftaris,Jana Zujovic,Aggelos K. Katsaggelos +2 more
- 12 Dec 2008
TL;DR: Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography.
6
Multiple view image denoising using 3D focus image stacks
TL;DR: A novel multi-view image denoising algorithm using 3D focus image stacks (3DFIS) to exploit image redundancy within and across views and is superior over various existing state-of-the-art approaches in terms of both visual and quantitative performance.
6
Robust Noise Estimation Based on Noise Injection
Chongwu Tang,Xiaokang Yang,Guangtao Zhai +2 more
- 01 Jan 2014
TL;DR: A novel noise level estimation algorithm is proposed by investigating the distribution of local variances in natural images and can reduce the detrimental influence of textural image regions effectively and therefore relieves overestimation of the noise variance.
6
References
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
30.6K
Numerical recipes in C
William H. Press,Saul A. Teukolsky,William T. Vetterling,Brian P. Flannery +3 more
- 01 Jan 1994
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
Segmentation through variable-order surface fitting
Paul J. Besl,Ramesh Jain +1 more
TL;DR: A piecewise-smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete, noiseless image reconstruction based on the extracted functions and regions.
Fast Noise Variance Estimation
TL;DR: The paper presents a fast and simple method for estimating the variance of additive zero mean Gaussian noise in an image that requires only the use of a 3 A— 3 mask followed by a summation over the image or a local neighborhood.
561
•Book
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.