Influence of multiplicative noise variance evaluation accuracy on mm-band SLAR image filtering efficiency

2004 ◽  
Author(s):  
S.K. Abramov ◽  
W. Lukin ◽  
N.N. Ponomarenko ◽  
K.O. Egiazarian ◽  
O.B. Pogrebnyak
Keyword(s):  
Multiplicative Noise ◽  
Image Filtering ◽  
Noise Variance ◽  
Filtering Efficiency ◽  
Evaluation Accuracy

A Nonmodel Dual-Tree Wavelet Thresholding for Image Denoising Through Noise Variance Optimization Based on Improved Chaotic Drosophila Algorithm

2017 ◽  
Vol 31 (08) ◽  
pp. 1754015 ◽  
Author(s):  
Lin Zhang ◽  
Xiaomou Zhou ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Xinhua Liu
Keyword(s):  
Wavelet Transform ◽  
Multiplicative Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Model ◽  
Mining Machine ◽  
Discrete Wavelet ◽  
Wavelet Thresholding ◽  
Noise Variance ◽  
Shrinkage Function

To remove image noise without considering the noise model, a dual-tree wavelet thresholding method (CDOA-DTDWT) is proposed through noise variance optimization. Instead of building a noise model, the proposed approach using the improved chaotic drosophila optimization algorithm (CDOA), to estimate the noise variance, and the estimated noise variance is utilized to modify wavelet coefficients in shrinkage function. To verify the optimization ability of the improved CDOA, the comparisons with basic DOA, GA, PSO and VCS are performed as well. The proposed method is tested to remove addictive noise and multiplicative noise, and denoising results are compared with other representative methods, e.g. Wiener filter, median filter, discrete wavelet transform-based thresholding (DWT), and nonoptimized dual-tree wavelet transform-based thresholding (DTDWT). Moreover, CDOA-DTDWT is applied as pre-processing utilization for tracking roller of mining machine as well. The experiment and application results prove the effectiveness and superiority of the proposed method.

scholarly journals Digital Path Approach Despeckle Filter for Ultrasound Imaging and Video

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Marek Szczepański ◽  
Krystian Radlak
Keyword(s):  
Multiplicative Noise ◽  
Weighted Average ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Video Streams ◽  
Similarity Functions ◽  
Different Types ◽  
Filtering Efficiency ◽  
Filtering Technique ◽  
Time Image

We propose a novel filtering technique capable of reducing the multiplicative noise in ultrasound images that is an extension of the denoising algorithms based on the concept of digital paths. In this approach, the filter weights are calculated taking into account the similarity between pixel intensities that belongs to the local neighborhood of the processed pixel, which is called a path. The output of the filter is estimated as the weighted average of pixels connected by the paths. The way of creating paths is pivotal and determines the effectiveness and computational complexity of the proposed filtering design. Such procedure can be effective for different types of noise but fail in the presence of multiplicative noise. To increase the filtering efficiency for this type of disturbances, we introduce some improvements of the basic concept and new classes of similarity functions and finally extend our techniques to a spatiotemporal domain. The experimental results prove that the proposed algorithm provides the comparable results with the state-of-the-art techniques for multiplicative noise removal in ultrasound images and it can be applied for real-time image enhancement of video streams.

Joint Blind Estimation of Channel Length and Noise Variance in OFDM Systems

2011 ◽  
Vol E94-B (12) ◽  
pp. 3614-3617
Author(s):  
Bin SHENG ◽  
Pengcheng ZHU ◽  
Xiaohu YOU
Keyword(s):  
Channel Length ◽  
Blind Estimation ◽  
Ofdm Systems ◽  
Noise Variance

A METHOD FOR AUTOMATIC BLIND ESTIMATION OF ADDITIVE NOISE VARIANCE IN DIGITAL IMAGES

2010 ◽  
Vol 69 (19) ◽  
pp. 1681-1702
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
A. V. Popov ◽  
P. Ye. Eltsov ◽  
Benoit Vozel ◽  
...  
Keyword(s):  
Additive Noise ◽  
Digital Images ◽  
Blind Estimation ◽  
Noise Variance

Methods for Blind Evaluation of Noise Variance in Multichannel Optical and Radar Images

2006 ◽  
Vol 65 (6) ◽  
pp. 527-556 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
N. N. Ponomarenko ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Noise Variance ◽  
Radar Images

AUTOMATIC ESTIMATION OF SPATIALLY CORRELATED NOISE VARIANCE IN SPECTRAL DOMAIN FOR IMAGES

2014 ◽  
Vol 73 (6) ◽  
pp. 511-527 ◽  
Author(s):  
V.V. Abramova ◽  
S. K. Abramov ◽  
V. V. Lukin ◽  
A. A. Roenko ◽  
Benoit Vozel
Keyword(s):  
Spectral Domain ◽  
Correlated Noise ◽  
Noise Variance ◽  
Spatially Correlated ◽  
Automatic Estimation ◽  
Spatially Correlated Noise
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