Granular filter in medical image noise suppression and edge preservation

In crack image recognition, Donoho’s universal wavelet threshold de-noising method appears "over-kill" phenomenon due to the lack of self-adaptability of threshold value; hence the image may lose its edge details. To handle this problem, the Donoho’s universal threshold and threshold function are improved and an adaptive determination method of threshold coefficient is introduced in this paper. Experimental results shows that the proposed method can effectively remove digital image noise and achieve a better edge protection, higher edge preservation index, better visual effects and higher peak signal-to-noise ratio.

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SAR image noise suppression of BEMD by the kernel principle component analysis

IET Image Processing ◽

10.1049/ipr2.12015 ◽

2020 ◽

Author(s):

Changjun Huang ◽

Xinghua Zhou ◽

Jiyuan Hu ◽

Qingshan Zhou

Keyword(s):

Principle Component Analysis ◽

Noise Suppression ◽

Component Analysis ◽

Image Noise ◽

Sar Image ◽

Principle Component

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Speckle Noise Suppression in SAR Images Using a Three-Step Algorithm

Sensors ◽

10.3390/s18113643 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3643 ◽

Cited By ~ 2

Author(s):

Ze Yu ◽

Wenqi Wang ◽

Chunsheng Li ◽

Wei Liu ◽

Jian Yang

Keyword(s):

Noise Suppression ◽

Minimum Mean Square Error ◽

Image Interpretation ◽

Speckle Noise ◽

Bias Reduction ◽

Edge Preservation ◽

Improved Performance ◽

Step Algorithm ◽

The Impact ◽

Speckle Suppression

Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and complicate image interpretation. This study proposes a novel three-step approach based on the conventional probabilistic patch-based (PPB) algorithm to minimize the impact of bright structures on speckle suppression. The first step improves the calculation accuracy of the weight by pre-processing speckle noise with a linear minimum mean-square error filter and reassessing similarity between pixels. In the second step, an iterative method is developed to avoid interfering with bright structures and acquires a more accurate homogeneous factor by adaptively changing the size of the search window. In the final step, the spreading and blurring of bright structures is corrected using a modified bias-reduction technique. Experimental results demonstrate the proposed algorithm has improved performance for both speckle suppression and preservation of edges and textures, evaluated by indicators including the equivalent number of looks, the edge preservation index, the mean, and standard deviation of ratio images.

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Range image noise suppression in laser imaging system

Optics & Laser Technology ◽

10.1016/j.optlastec.2008.05.029 ◽

2009 ◽

Vol 41 (2) ◽

pp. 140-147 ◽

Cited By ~ 16

Author(s):

Qi Wang ◽

Qi Li ◽

Zhe Chen ◽

Jianfeng Sun ◽

Rui Yao

Keyword(s):

Noise Suppression ◽

Imaging System ◽

Image Noise ◽

Range Image ◽

Laser Imaging

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Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation

IEEE Transactions on Image Processing ◽

10.1109/tip.2008.2001398 ◽

2008 ◽

Vol 17 (9) ◽

pp. 1522-1539 ◽

Cited By ~ 58

Author(s):

J.M. Sanches ◽

J.C. Nascimento ◽

J.S. Marques

Keyword(s):

Noise Reduction ◽

Medical Image ◽

Lyapunov Equation ◽

Image Noise ◽

Image Noise Reduction

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Curvelet Denoising with Improved Thresholds for Application on Ultrasound Images

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2013.1187 ◽

2013 ◽

pp. 156-161

Author(s):

Tajinder Kaur ◽

Dinesh Kumar ◽

Ekta Walia ◽

Manjit Sandhu

Keyword(s):

Medical Image ◽

Noise Suppression ◽

Acoustic Noise ◽

Medical Image Processing ◽

Curvelet Transform ◽

Speckle Noise ◽

Ultrasound Images ◽

Challenging Problem ◽

Effective Suppression ◽

Ultrasonic Images

In medical image processing, image denoising has become a very essential exercise all through the diagnose. Negotiation between the preservation of useful diagnostic information and noise suppression must be treasured in medical images. In case of ultrasonic images a special type of acoustic noise, technically known as speckle noise, is the major factor of image quality degradation. Many denoising techniques have been proposed for effective suppression of speckle noise. Removing noise from the original image or signal is still a challenging problem for researchers. In this paper, a Curvelet transform based denoising with improved thresholds is proposed for ultrasound images.

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Revisiting the digital noise reduction in automatic contouring of “ice-water” objects

Arctic and Antarctic Research ◽

10.30758/0555-2648-2020-66-1-102-114 ◽

2020 ◽

Vol 66 (1) ◽

pp. 102-114

Author(s):

K. N. Zvyagin ◽

D. D. Maltsev

Keyword(s):

Unmanned Aerial Vehicle ◽

Aerial Photography ◽

Noise Suppression ◽

Structural Features ◽

Image Noise ◽

Practical Implementation ◽

Haar Wavelet Transform ◽

Northern Sea Route ◽

Aerial Vehicle ◽

Ice Water

This work describes the practical implementation of the method for digital noise suppression during processing images containing ice information to recognize automatically the contours of «ice-water» objects during aerial photography. Images containing ice information have special characteristic structural features related to noise, e.g.granularity, glare, ice crumbs. This makes difficult or even impossible to recognize automatically the contours of ice-water objects. It is known that the success of the application of edge recognition methods depends on how much image noise is reduced. The paper discusses the construction method for the management of noise. The method is based on the sequential application of the Haar wavelet transform denoising using thresholding, clustering by k-means method. For the subsequent automatic construction of ice floes contours the Sobel operator is applied.The aim of the work is to develop a method capable to process digital images effectively that contain ice information with strong digital noise. In this work we treated the images of one-year ice containing strong digital image noise in the form of granularity and in the form of ice crumbs. A description of the features of each of the steps of the proposed method and practical application is given.As a result, the method was developed for processing images of ice information containing digital noise in absolute value commensurate with the basic data. It was noted that the use of the k-means method expands the scope. The k-rare method allows more detailed processing of ice information and distinguishes not only the contours of ice-water objects, but also the contours of ice crumbs.The conclusion formulates the main advantages of the method and the possible application of the algorithm in the process of local exploration of the ice conditions of the Northern Sea Route channel using unmanned aerial vehicle for aerial photography. The usage of unmanned aerial vehicle for aerial photography will increase the frequency of weather forecast updates and predict the appearance of ice objects at the ship’s heading. That will allow us to select the safest and most economical efficient route along the Northern Sea Route.The authors have no competing interests.

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Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection

Chemosensors ◽

10.3390/chemosensors9090268 ◽

2021 ◽

Vol 9 (9) ◽

pp. 268

Author(s):

Le Zhang ◽

Lixian Liu ◽

Huiting Huan ◽

Xukun Yin ◽

Xueshi Zhang ◽

...

Keyword(s):

Noise Suppression ◽

Dynamic Range ◽

Ambient Air ◽

Gas Analysis ◽

Gas Detection ◽

Trace Gas ◽

Trace Gas Detection ◽

Edge Preservation ◽

Local Patch ◽

Non Local

A non-local patch regression (NLPR) denoising-enhanced differential broadband photoacoustic (PA) sensor was developed for the high-sensitive detection of multiple trace gases. Using the edge preservation index (EPI) and signal-to-noise ratio (SNR) as a dual-criterion, the fluctuation was dramatically suppressed while the spectral absorption peaks were maintained by the introduction of a NLPR algorithm. The feasibility of the broadband framework was verified by measuring the C2H2 in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10−11 cm−1·W·Hz−1/2 was obtained with a 30-mW globar source and a SNR improvement factor of 23. Furthermore, the simultaneous multiple-trace-gas detection capability was determined by measuring C2H2, H2O, and CO2. Following the guidance of single-component processing, the NLPR processed results showed higher EPI and SNR compared to the spectra denoised by the wavelet method and the non-local means algorithm. The experimentally determined SNRs of the C2H2, H2O, and CO2 spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10−11 cm−1·W·Hz−1/2 for C2H2. The NLPR algorithm presented good performance in noise suppression and absorption peak fidelity, which offered a higher dynamic range and was demonstrated to be an effective approach for trace gas analysis.

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