Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise

Kuan, D.; Sawchuk, Alexander A.; Strand, T. C.; Chavel, Pierre

doi:10.1109/tpami.1985.4767641

Cited by 1,304 publications

(547 citation statements)

References 15 publications

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“…Beyond variational approaches there exist other techniques to remove multiplicative noise, e.g., local linear minimum mean square error approaches [47,46] or anisotropic diffusion methods [66,1,44] which will not be addressed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

2009

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In this paper, we consider a variational restoration model consisting of the I-divergence as data fitting term and the total variation semi-norm or nonlocal means as regularizer for removing multiplicative Gamma noise. Although the I-divergence is the typical data fitting term when dealing with Poisson noise we substantiate why it is also appropriate for cleaning Gamma noise. We propose to compute the minimizers of our restoration functionals by applying Douglas-Rachford splitting techniques, resp. alternating direction methods of multipliers. For a particular splitting, we present a semi-implicit scheme to solve the involved nonlinear systems of equations and prove its Q-linear convergence. Finally, we demonstrate the performance of our methods by numerical examples.

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Section: Introductionmentioning

confidence: 99%

Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

2009

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“…Adaptive filters adapt themselves to the local texture information surrounding a central pixel in order to calculate a new pixel value. Adaptive filters generally incorporate the Kuan filter, Lee filter, Frost filter, Gamma MAP filters [3], [4], [5]. These filters made obvious their superiority measured up to low pass filters, since they have taken into account the local statistical properties of the image.…”

Section: Introductionmentioning

confidence: 99%

Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance

Sudha¹,

Suresh²,

Sukanesh³

2009

IJCTE

145

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Abstract-In medical image processing, image denoising has become a very essential exercise all through the diagnose. Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can restrain information which is valuable for the general practitioner. Consequently medical images are very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in ultrasound images. Quantitative and qualitative comparisons of the results obtained by the proposed method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction Index Terms-Medical imaging, Speckle noise, Ultrasound images, Wavelet Thresholding.

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“…Every individual photon could be a quantum (specific quantity) of energy. It's the quantum structure of associate x-ray beam that makes quantum noise [8].…”

Section: Poisson Noisementioning

confidence: 99%

Reduction of types of Noises in dental Images

Subbuthai¹,

Kavithabharathi²,

Muruganand³

2013

IJCATR

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Abstract---This paper presents a filter for restoration of Dental images that are highly corrupted by salt and pepper noise and speckle noise, Poisson noise. After detecting and correcting the noisy pixel, the proposed filter is able to suppress noise level. In this paper for each noise proposed different type of filter and compare these three types of filter with their PSNR value and MSE value and SNR value. After filtering stage maximum detected noise pixels will be filtered and simulation results show the filtered image.

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Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise

Cited by 1,304 publications

References 15 publications

Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance

Reduction of types of Noises in dental Images

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