Estimation of Noise in Images: An Evaluation

Olsen, S

doi:10.1006/gmip.1993.1022

Cited by 77 publications

(48 citation statements)

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“…Recently, in [8], a block-based noise estimation method was proposed, with a new measure for determining intensity-homogeneous blocks and a structure analyzer for rejecting blocks with structure. They showed to outperform the methods of [4] and [10].…”

Section: Related Workmentioning

confidence: 98%

“…Some of those noise estimation approaches were evaluated and compared in [10], where it was concluded that the averaging approach, which is based on first suppressing image structures by prefiltering and then computing the noise variance, provides the most reliable results for a wide range of noise levels and images with different content. Recently, in [8], a block-based noise estimation method was proposed, with a new measure for determining intensity-homogeneous blocks and a structure analyzer for rejecting blocks with structure.…”

Section: Related Workmentioning

confidence: 99%

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Noise estimation for video processing based on spatio-temporal gradients

Zlokolica

Pižurica

Philips

2006

IEEE Signal Process. Lett.

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Abstract-We propose an efficient and accurate wavelet-based noise estimation method for white Gaussian noise in video sequences. The proposed method analyzes the distribution of spatial and temporal gradients in the video sequence in order to estimate the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the errors due to the spatio-temporal image sequence content, by a novel correction function. The spatial and temporal gradients are determined from the finest scale of the spatial and temporal wavelet transform, respectively. The main application of the noise estimation algorithm is in wavelet-based video processing. The results show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to varying spatio-temporal content and noise level.Index Terms-Noise estimation, video analysis and processing, wavelets.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Noise estimation for video processing based on spatio-temporal gradients

Zlokolica

Pižurica

Philips

2006

IEEE Signal Process. Lett.

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“…4a, was chosen for analysis and both the original NLM algorithm and the statistically motivated variant incorporating scale adjustment were applied. Noise was estimated directly ( [14]) from the image in the region of the brain and patch similarities computed over a 7x7 region (shown in previous work [11] to provide an optimal balance between accuracy and required processor power for the original NLM algorithm).…”

Section: Clinical Mr Datamentioning

confidence: 99%

“…This measure is referred to as the residual outlier measure (ROM) and, since the expected value for a perfect noise filter can be calculated as the two-tailed integral of the noise distribution beyond 3σ, any deviation from this expected value quantifies the number of pixels to which inappropriate smoothing is applied. The local image noise was estimated independently of the Monte-Carlo stability analysis using a technique based upon the distribution of local derivatives [14]. In order to provide a benchmark for the evaluation, it was also applied to three conventional noise filtering schemes.…”

Section: Quantitative Evaluation Of Non-local Meansmentioning

confidence: 99%

A statistical interpretation of non-local means

Thacker

Manjón

Bromiley

2008

5th International Conference on Visual Information Engineering (VIE 2008)

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Noise filtering is a common step in image processing, and is particularly effective in improving the subjective quality of images. A large number of techniques have been developed, many of which concentrate on the problem of removing noise without damaging small structures such as edges. One recent approach that demonstrates empirical merit is the non-local means (NLM) algorithm. However, in order to use noise filtering algorithms in quantitative or clinical image analysis tasks an understanding of their behaviour that goes beyond subjective appearance must be developed. The purpose of this paper is to investigate the statistical basis of NLM in order to attempt to understand the conditions required for its use. The theory is illustrated on synthetic data and clinical MR images of the brain.

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“…In [12] several other models are presented for estimating image noise. When maximising autocorrelation the MNF analysis qualifies as an Independent Components Analysis (ICA) similar to the Molgedy-Schuster algorithm [14], see [5].…”

Section: Minimum Noise Fractions Transformationmentioning

confidence: 99%

A Noise Robust Statistical Texture Model

Hilger

Stegmann

Larsen

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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Abstract. This paper presents a novel approach to the problem of obtaining a low dimensional representation of texture (pixel intensity) variation present in a training set after alignment using a Generalised Procrustes analysis. We extend the conventional analysis of training textures in the Active Appearance Models segmentation framework. This is accomplished by augmenting the model with an estimate of the covariance of the noise present in the training data. This results in a more compact model maximising the signal-to-noise ratio, thus favouring subspaces rich on signal, but low on noise. Differences in the methods are illustrated on a set of left cardiac ventricles obtained using magnetic resonance imaging.

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Estimation of Noise in Images: An Evaluation

Cited by 77 publications

References 0 publications

Noise estimation for video processing based on spatio-temporal gradients

Noise estimation for video processing based on spatio-temporal gradients

A statistical interpretation of non-local means

A Noise Robust Statistical Texture Model

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