Image De-Noising Method with Noise Control Materials Based on Wavelet Transform and Wiener Filter

2012 ◽  
Vol 459 ◽  
pp. 219-223
Author(s):  
Yuan Mei Wang ◽  
Tao Li
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
White Noise ◽  
Noise Control ◽  
Gaussian White Noise ◽  
Wiener Filter ◽  
Experimental Results ◽  
Wiener Filtering ◽  
High Image Quality ◽  
Image Edges

When image with Gaussian white noise being de-noised by wavelet threshold, there are some problems such as blurring and the loss of details of edges of image. To solve above problems, image de-noising method based on wavelet transform and Wiener filtering is proposed in the paper, first using wavelet threshold to de-noise, and then using Wiener filter to smooth the image so as to get high image quality. Experimental results show that this algorithm on de-noising proposed not only can effectively suppress Gaussian white noise, but also can well preserve the details of image edges

Research of Denoising Technology about Wavelet Analysis with Wiener Filter

2013 ◽  
Vol 333-335 ◽  
pp. 597-600
Author(s):  
Yao Bin Hu ◽  
Liang Bin Hu ◽  
Qiang Cheng
Keyword(s):  
Signal Processing ◽  
Wavelet Analysis ◽  
White Noise ◽  
Frequency Spectrum ◽  
Wiener Filter ◽  
Power Line ◽  
Wiener Filtering ◽  
Power Line Communication ◽  
Denoising Method

Interfering noise of power line is one of the important factors which affects the quality of power line communication (PLC). Its frequency spectrum has the character of the 1/f process and the great autocorrelation. The wavelet analysis is an important signal-processing tool. Selecting suitable wavelet analysis can turn non-white noise to white noise, followed by wiener filtering, we can achieve the purpose of denoising. This paper introduces a denoising method of combining wavelet analysis with wiener filtering. Experiment proves this method has a strong feasibility and practical value.

Image Denoising Based on K-SVD with Adaptive Dictionary Size

2014 ◽  
Vol 1049-1050 ◽  
pp. 1645-1649
Author(s):  
Mei Ling Fu ◽  
Zhi Ming Wang ◽  
Rui Xin Cao
Keyword(s):  
White Noise ◽  
Image Denoising ◽  
Noise Level ◽  
Gaussian White Noise ◽  
Experimental Results ◽  
Image Content ◽  
Denoising Method ◽  
Texture Information ◽  
Self Learning ◽  
Image Details

Image denoising method based on K-SVD self-learning dictionary can effectively filter Gaussian white noise in an image and retain image details and texture information. This paper proposed an improved denoising algorithm which was based on K-SVD algorithm, but with adaptive dictionary size. Depending on the complexity of image content and the noise level, our algorithm determines the size of the dictionary adaptively. Experimental results show that proposed algorithm can reduce the number of entries of the dictionary significantly for the simple images, and increase the number of entries for the complex images. Both the efficiency and the denoising performance are improved compared with original K-SVD algorithm.

An Improved Texture Compression Method for Graphics Hardware

2011 ◽  
Vol 63-64 ◽  
pp. 444-448
Author(s):  
Hai Ying Chen ◽  
Yin Yin Zhou
Keyword(s):  
Image Quality ◽  
Experimental Results ◽  
Graphics Hardware ◽  
Bilinear Interpolation ◽  
Compression Method ◽  
Compression Scheme ◽  
High Image Quality

In this paper, we proposed an improved texture compression method for graphics hardware. We first give a detail introduction for the texture compression methods which are popular now. Then, an MIPMap-based texture compression method is proposed that is hybrid compression scheme. For smooth area, we can only use 2Bytes to represent the 4*4 pixels block by bilinear interpolation. Otherwise, we will use iPACKMAN algorithm to deal with the noise areas. Actually, this method is feasible to be implemented by hardware since it is very simple. Experimental results show that our method can achieve high compress ratio and high image quality.

IMAGE ENHANCEMENT BY KRIGING AND FUZZY SETS

2000 ◽  
Vol 14 (08) ◽  
pp. 1025-1038 ◽  
Author(s):  
TUAN D. PHAM ◽  
MICHAEL WAGNER
Keyword(s):  
White Noise ◽  
Fuzzy Sets ◽  
Linear Combination ◽  
Gaussian White Noise ◽  
Wiener Filter ◽  
Spatial Filter ◽  
Gradual Transition ◽  
Kriging Method ◽  
Combination Scheme ◽  
Fuzzy Thresholding

A kriging method is presented as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. The concepts are based on the analysis of semivariances, the linear combination scheme of kriging, and fuzzy sets. Application of fuzzy sets allows a gradual transition between two boundaries of semivariance levels as a criterion for smoothing the pixel values. This fuzzy thresholding also allows some degree of flexibility to suit various desired results for particular problems. Experimental results obtained by the fuzzy kriging filter are smoother and still preserve edges compared with those by the adaptive Wiener filter.

scholarly journals Coverless Information Hiding Based on WGAN-GP Model

2021 ◽  
Vol 13 (4) ◽  
pp. 57-70
Author(s):  
Xintao Duan ◽  
Baoxia Li ◽  
Daidou Guo ◽  
Kai Jia ◽  
En Zhang ◽  
...  
Keyword(s):  
Image Quality ◽  
Information Hiding ◽  
Experimental Results ◽  
Natural Image ◽  
High Image Quality ◽  
Secret Image ◽  
Secret Information ◽  
Coverless Information Hiding ◽  
Gp Model

Steganalysis technology judges whether there is secret information in the carrier by monitoring the abnormality of the carrier data, so the traditional information hiding technology has reached the bottleneck. Therefore, this paper proposed the coverless information hiding based on the improved training of Wasserstein GANs (WGAN-GP) model. The sender trains the WGAN-GP with a natural image and a secret image. The generated image and secret image are visually identical, and the parameters of generator are saved to form the codebook. The sender uploads the natural image (disguise image) to the cloud disk. The receiver downloads the camouflage image from the cloud disk and obtains the corresponding generator parameter in the codebook and inputs it to the generator. The generator outputs the same image for the secret image, which realized the same results as sending the secret image. The experimental results indicate that the scheme produces high image quality and good security.

Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

1977 ◽  
Vol 35 ◽  
pp. 214-217
Author(s):  
F. A. Heckman ◽  
E. Redman ◽  
J.E. Connolly
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Exposure Time ◽  
Image Contrast ◽  
Automated Image Analysis ◽  
Practical Application ◽  
Factors Affecting ◽  
High Image Quality ◽  
Qualitative Evidence ◽  
Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

Estimation of Heart Rate Variability Fluctuations by Wavelet Transform

2011 ◽  
Vol 57 (3) ◽  
pp. 395-400 ◽  
Author(s):  
Anton Popov ◽  
Yevgeniy Karplyuk ◽  
Volodymyr Fesechko
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Experimental Results ◽  
Dyadic Wavelet ◽  
Heart Rate Fluctuations ◽  
Slow Heart Rate ◽  
Dyadic Wavelet Transform

Estimation of Heart Rate Variability Fluctuations by Wavelet TransformTechnique for separate estimation of fast and slow fluctuations in the heart rate signal is developed. The orthogonal dyadic wavelet transform is used to separate the slow heart rate changes in approximation part of decomposition and fast changes in detail parts. Experimental results using the recordings from persons practicing Chi meditation demonstrated the applicability of estimation heart rate fluctuations with the proposed approach.

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