Non-local Mean Filter for Suppression of Speckle Noise in Ultrasound Images

Background: An approach based on QR decomposition, to remove speckle noise from medical ultrasound images, is presented in this paper. Methods: The speckle noisy image is segmented into small overlapping blocks. A global covariance matrix is calculated by averaging the corresponding covariances of the blocks. QR decomposition is applied to the global covariance matrix. To filter out speckle noise, the first subset of orthogonal vectors of the Q matrix is projected onto the signal subspace. The proposed approach is compared with five benchmark techniques; Homomorphic Wavelet Despeckling (HWDS), Speckle Reducing Anisotropic Diffusion (SRAD), Frost, Kuan and Probabilistic Non-Local Mean (PNLM). Results and Conclusion: When applied to different simulated and real ultrasound images, the QR based approach has secured maximum despeckling performance while maintaining optimal resolution and edge detection, and that is regardless of image size or nature of speckle; fine or rough.

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Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Journal of Visual Communication and Image Representation ◽

10.1016/j.jvcir.2014.10.006 ◽

2015 ◽

Vol 26 ◽

pp. 317-328 ◽

Cited By ~ 6

Author(s):

Hak Gu Kim ◽

Seung Ji Seo ◽

Byung Cheol Song

Keyword(s):

Video Sequences ◽

Mean Filter ◽

Local Mean ◽

Non Local

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Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Entropy ◽

10.3390/e20110827 ◽

2018 ◽

Vol 20 (11) ◽

pp. 827 ◽

Cited By ~ 1

Author(s):

Chundi Jiang ◽

Wei Yang ◽

Yu Guo ◽

Fei Wu ◽

Yinggan Tang

Keyword(s):

Image Segmentation ◽

Relative Entropy ◽

Spatial Information ◽

Two Dimensional ◽

Nonlocal Means ◽

Mean Filter ◽

Filter Process ◽

Local Mean ◽

Non Local ◽

Correlation Information

Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

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Improved non-local self-similarity measures for effective speckle noise reduction in ultrasound images

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105670 ◽

2020 ◽

Vol 196 ◽

pp. 105670

Author(s):

Fuyuan Mei ◽

Dong Zhang ◽

Yan Yang

Keyword(s):

Noise Reduction ◽

Similarity Measures ◽

Speckle Noise ◽

Ultrasound Images ◽

Self Similarity ◽

Non Local ◽

Speckle Noise Reduction

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Spatially-guided non-local mean filter for denoising of clinical whole-body PET images

2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) ◽

10.1109/nssmic.2018.8824613 ◽

2018 ◽

Cited By ~ 1

Author(s):

Hossein Arabi ◽

Habib Zaidi

Keyword(s):

Whole Body ◽

Mean Filter ◽

Local Mean ◽

Non Local

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An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

10.4018/978-1-6684-2408-7.ch055 ◽

2022 ◽

pp. 1157-1173

Author(s):

Bibekananda Jena ◽

Punyaban Patel ◽

G.R. Sinha

Keyword(s):

Neural Network ◽

Impulse Noise ◽

Noise Suppression ◽

Back Propagation ◽

Feed Forward Neural Network ◽

Suppression Technique ◽

Mean Filter ◽

Local Mean ◽

Non Local ◽

A New Technique

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

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