scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

scholarly journals Total Variation Wavelet-Based Medical Image Denoising

2006 ◽  
Vol 2006 ◽  
pp. 1-6 ◽  
Author(s):  
Yang Wang ◽  
Haomin Zhou
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Medical Image ◽  
Medical Images ◽  
Stopping Time ◽  
Noise Removal ◽  
Total Variation Minimization ◽  
Time Criterion ◽  
Minimization Scheme

We propose a denoising algorithm for medical images based on a combination of the total variation minimization scheme and the wavelet scheme. We show that our scheme offers effective noise removal in real noisy medical images while maintaining sharpness of objects. More importantly, this scheme allows us to implement an effective automatic stopping time criterion.

scholarly journals Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation

2021 ◽  
Vol 13 (4) ◽  
pp. 827
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Hyperspectral Image ◽  
Noise Removal ◽  
Low Rank ◽  
Matrix Recovery ◽  
Tv Regularization ◽  
Rank Matrix ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.

scholarly journals Edge-Preserving Median Filter and Weighted Coding with Sparse Nonlocal Regularization for Low-Dose CT Image Denoising Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Low Dose ◽  
Median Filter ◽  
Similarity Index ◽  
Ct Image ◽  
Edge Preserving ◽  
Low Dose Ct ◽  
Nonlocal Regularization

The impulse noise in CT image was removed based on edge-preserving median filter algorithm. The sparse nonlocal regularization algorithm weighted coding was used to remove the impulse noise and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. It was found that in nine different proportions of Gaussian noise and salt-and-pepper noise in Shepp-Logan image and CT image processing, the PSNR and SSIM values of the proposed denoising algorithm based on edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were significantly higher than those of using EP median filter and WESNR alone. It was shown that the weighted coding algorithm based on edge-preserving median filtering and sparse nonlocal regularization had potential application value in low-dose CT image denoising.

Medical Image Denoising Based on Soft Thresholding Using Biorthogonal Multiscale Wavelet Transform

2014 ◽  
Vol 14 (01n02) ◽  
pp. 1450002 ◽  
Author(s):  
Om Prakash ◽  
Ashish Khare
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Noise Removal ◽  
Phase Characteristic ◽  
Biorthogonal Wavelet ◽  
Linear Phase ◽  
Soft Thresholding ◽  
The Absolute

Recorded medical images often represent a degraded version of the original scene due to imperfections in electronic or photographic medium used. The degradations may have many causes, but two dominant degradations are noise and blur. Restoration of blurred and noisy medical images is of fundamental importance in several medical imaging applications. Most of the medical image denoising techniques need removal of blur before the denoising. Denoising of medical images in presence of blur is a hard problem. Most of the wavelet transform-based denoising techniques use the orthonormal wavelets and suitable for image corrupted with only additive white Gaussian noise. In the present work, we have proposed a denoising algorithm for medical images based on the lifting-scheme and linear phase characteristic of biorthogonal wavelet transform. A level-dependent soft thresholding function has been used which is based on the standard deviation, the absolute mean and the absolute median of the wavelet coefficients. The linear phase characteristic of the biorthogonal filters used in denoising reduces the distortions at edge points of image. Also, the lifting schemes of the biorthogonal wavelet filters make the algorithm efficient and applicable in real time. Experimental results show that the proposed denoising method outperform standard wavelet, complex wavelet and curvelet-based denoising techniques in terms of the SNR and PSNR (in dB) and it offers effective noise removal from noisy medical images while maintaining sharpness of objects in the image.

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES

2007 ◽  
Vol 07 (04) ◽  
pp. 663-687 ◽  
Author(s):  
ASHISH KHARE ◽  
UMA SHANKER TIWARY
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Universal Method ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Daubechies Complex Wavelet Transform ◽  
Complex Wavelet

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

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