Filtering noise from images with wavelet transforms

Weaver, John B.; Xu, Yansun; Healy, Dennis M.; Cromwell, Laurence D.

doi:10.1002/mrm.1910210213

Cited by 250 publications

(130 citation statements)

References 9 publications

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“…Wavelet based multi-scale methods have been extensively used for image enhancement and denoising problems [2][3][4][5][6]. Unlike traditional single scale methods of analysis, which can be ad hoc for denoising, wavelet expansions offer the possibility of separating features of interest and noise components into distinct sub-band coefficients.…”

Section: Methodsmentioning

confidence: 99%

De-noising SPECT/PET Images Using Cross-Scale Regularization

Jin

Angelini

Esser

et al. 2003

Lecture Notes in Computer Science

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Abstract. De-noising of SPECT and PET images is a challenging task due to the inherent low signal-to-noise ratio of acquired data. Wavelet based multiscale denoising methods typically apply thresholding operators on sub-band coefficients to eliminate noise components in spatial-frequency space prior to reconstruction. In the case of high noise levels, detailed scales of sub-band images are usually dominated by noise which cannot be easily removed using traditional thresholding schemes. To address this issue, a cross-scale regularization scheme is introduced, which takes into account cross-scale coherence of structured signals. Preliminary results show promising performance in denoising clinical SPECT and PET images for liver and brain studies. Wavelet thresholding was also compared to denoising with a brushlet expansion. The proposed regularization scheme eliminates the need for threshold parameter settings, making the denoising process less tedious and suitable for clinical practice.

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Section: Methodsmentioning

confidence: 99%

De-noising SPECT/PET Images Using Cross-Scale Regularization

Jin

Angelini

Esser

et al. 2003

Lecture Notes in Computer Science

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“…Denoising with wavelets can be traced back to the work by Weaver et al [170] (and even earlier to Witkin [172]), and was later on popularized by Donoho and Johnstone [73,76]. Even then, sophisticated use of overcomplete expansions showed excellent results, and thus one of the first works on denoising with frames is [177], where the authors combined the overcomplete expansion with a variation of the technique from [129] to reconstruct the image from its wavelet maxima.…”

Section: Denoisingmentioning

confidence: 99%

An Introduction to Frames

Kovačević

Chebira

2007

FNT in Signal Processing

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“…By using the receiver coil sensitivity profiles, the priori information regarding the image noise level spatial distribution is obtained and it is utilized for the local adjustment of the anisotropic diffusion filter. A simple wavelet based noise reduction was proposed by Weaver et al [8]. The main drawback of the method was small structures similar in size to noise where also eliminated.…”

Section: Introductionmentioning

confidence: 99%

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Mohan¹,

Krishnaveni²,

Guo³

2013

Measurement Science Review

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In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.

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Filtering noise from images with wavelet transforms

Cited by 250 publications

References 9 publications

De-noising SPECT/PET Images Using Cross-Scale Regularization

De-noising SPECT/PET Images Using Cross-Scale Regularization

An Introduction to Frames

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

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