scholarly journals Assessment of random-noise contamination in digital images via testing on wavelet coefficients

2013 ◽  
Vol 6 (1) ◽  
pp. 117-135
Author(s):  
Diego Maldonado ◽  
Sharad Silwal ◽  
Haiyan Wang
Keyword(s):  
Digital Images ◽  
Random Noise ◽  
Wavelet Coefficients ◽  
Noise Contamination

scholarly journals ON ESTIMATES OF COEFFICIENTS OF GENERALIZED ATOMIC WAVELETS EXPANSIONS AND THEIR APPLICATION TO DATA PROCESSING

2020 ◽  
pp. 44-57 ◽  
Author(s):  
Victor Olexandrovych Makarichev ◽  
Vladimir Vasilyevich Lukin ◽  
Iryna Victorivna Brysina
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Signal To Noise Ratio ◽  
Expected Value ◽  
Wavelet Expansion ◽  
Correction Coefficient ◽  
Quality Loss ◽  
Wavelet Coefficients ◽  
Absolute Deviation ◽  
Real Value

Discrete atomic compression (DAC) of digital images is considered. It is a lossy compression algorithm. The aim of this paper is to obtain a mechanism for control of quality loss. Among a large number of different metrics, which are used to assess loss of quality, the maximum absolute deviation or the MAD-metric is chosen, since it is the most sensitive to any even the most minor changes of processed data. In DAC, the main loss of quality is got in the process of quantizing atomic wavelet coefficients that is the subject matter of this paper. The goal is to investigate the effect of the quantization procedure on atomic wavelet coefficients. We solve the following task: to obtain estimates of these coefficients. In the current research, we use the methods of atomic function theory and digital image processing. Using the properties of the generalized atomic wavelets, we get  estimates of generalized atomic wavelet expansion coefficients. These inequalities provide dependence of quality loss measured by the MAD-metric on the parameters of quantization in the form of upper bounds. They are confirmed by the DAC-processing of the test images. Also, loss of quality measured by root mean square (RMS) and peak signal to noise ratio (PSNR) is computed. Analyzing the results of experiments, which are carried out using the computer program "Discrete Atomic Compression: Research Kit", we obtain the following results: 1) the deviation of the expected value of MAD from its real value in some cases is large; 2) accuracy of the estimates depends on parameters of quantization, as well as depth of atomic wavelet expansion and type of the digital image (full color or grayscale); 3) discrepancies can be reduced by applying a correction coefficient; 4) the ratio of the expected value of MAD to its real value behaves relatively constant and the ratio of the expected value of MAD to RMS and PSNR do not. Conclusions: discrete atomic compression of digital images in combination with the proposed method of quality loss control provide obtaining results of the desired quality and its further development, research and application are promising.

Auto-Layering Wavelet Transfer to Remove the Circumstance Effect and Noise

2013 ◽  
Vol 798-799 ◽  
pp. 624-629
Author(s):  
Pang Da Dai ◽  
Yu Jun Zhang ◽  
Chang Hua Lu ◽  
Yi Zhou ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Relative Error ◽  
Light Propagation ◽  
Digital Images ◽  
Energy Ratio ◽  
Light Sources ◽  
Wavelet Coefficients ◽  
Multi Scale ◽  
Transfer Method ◽  
Night Condition

The accuracy of visibility measurement from night light sources image is usually affected by the circumstance light and noise. This paper presents an auto-layering wavelet transfer method to remove the circumstance effect and noise simultaneously. Firstly, the light propagation through the fog at night condition is formulized, where the model and features of night image with circumstance effect and noise is given. Secondly, we propose to use multi-scale features of wavelet transfer to decompose the image to remove the circumstance effect and noise, where an auto-layering method is used based on the energy ratio of wavelet coefficients. Experiments show that our method is able to remove the circumstance effect and noise simultaneously and to adjust the decomposed layering number automatically. Our method is not only suitable for many wavelet functions, but also preserves the light sources as well as their glows in the digital images. The relative error of using db4 is 3.16%, and the relative error of using sym2 is 2.02%.

scholarly journals ROBUST WATERMARKING TECHNIQUE USING DIFFERENT WAVELET DECOMPOSITION LEVELS FOR SIGNATURE IMAGE PROTECTION

2017 ◽  
Author(s):  
Rohit M. Thanki ◽  
Ved Vyas Dwivedi ◽  
Komal R. Borisagar
Keyword(s):  
Wavelet Decomposition ◽  
Random Noise ◽  
Wavelet Coefficients ◽  
Robust Watermarking ◽  
Noise Pattern ◽  
Host Image ◽  
Watermark Embedding ◽  
Image Protection ◽  
Transform Coefficients ◽  
Pseudo Noise

This paper proposed a non-blind watermarking technique based on different wavelet decomposition levels for biometric image protection. In this technique, a biometric image is used as a watermark instead of a standard image, logo or random noise pattern type watermark. For watermark embedding, the original host image and the watermark biometric image are transformed into various levels of wavelet coefficients. The watermark biometric image is embedded into the host image by modifying the values of the wavelet coefficients of the host image using the values of wavelet coefficients of the watermark biometric image. Experimental results demonstrated that the proposed technique was able to withstand various watermarking attacks. The novelty of the proposed technique is that it is used to transform coefficients of the watermark biometric image instead of the Pseudo Noise sequences or any other feature extraction technique.  

Strong random noise attenuation by shearlet transform and time-frequency peak filtering

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. V319-V331 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transforms ◽  
Random Noise ◽  
Frequency Noise ◽  
Wavelet Coefficients ◽  
Cumulative Energy ◽  
Shearlet Transform ◽  
Time Frequency ◽  
Frequency Peak ◽  
Smoothing Window ◽  
Directional Wavelet

Directional wavelet transforms combined with coefficient thresholding are very competitive in denoising seismic signals. However, these techniques struggle when the coefficients of signal and noise have comparable magnitudes. To better address this problem, we have developed an improvement to this method by applying time-frequency peak filtering (TFPF) to the directional wavelet coefficients. TFPF consists of computing the instantaneous frequency of a frequency-modulated analytic signal. The use of a longer or shorter smoothing window helps to emphasize either signal or remove random noise. In our method, we use the shearlet transform as a directional wavelet transform and estimate signal dips based on the cumulative energy in each decomposition direction. TFPF is then applied to the fine-scale wavelet coefficients to enhance signal and remove high-frequency noise. Coefficient thresholding is applied to all other scales. Experimental results demonstrate that our algorithm can effectively eliminate strong random noise and preserve events of interest.

WAVELET DECOMPOSITION OF PSEUDO-MOTION IMAGE AND APPLICATION TO FREQUENCY SEGMENTATION

2011 ◽  
Vol 25 (07) ◽  
pp. 1089-1112 ◽  
Author(s):  
YUAN YAN TANG ◽  
LIMIN CUI ◽  
NING JIN
Keyword(s):  
Wavelet Decomposition ◽  
Digital Images ◽  
Experimental Results ◽  
Wavelet Coefficients ◽  
Moving Images ◽  
Novel Approach ◽  
Motion Images

In this paper, we propose a novel approach to frequency segmentation using wavelet decomposition of pseudo-motion image. In this way, a fixed image is translated such that a sequence of moving images is produced, which are called the 0 pseudo-motion images. In fact, we can consider the translation of a function to be a motion of eyeshot. When a function is translated, its wavelet coefficients will oscillate. From this property, we can detect the special areas of the image. Some experiments were conducted, which are used to find the position of license plates of vehicles in digital images. The experimental results demonstrate the performance of the method.

Induced Polarization and Resistivity of Second Potential Differences (SPD) with Focused Sources Applied to Environmental Problems

2019 ◽  
Vol 24 (1) ◽  
pp. 49-61
Author(s):  
Aide E. López-González ◽  
Andrés Tejero-Andrade ◽  
Jejanny L. Hernández-Martínez ◽  
Blanca Prado ◽  
René E. Chávez
Keyword(s):  
Apparent Resistivity ◽  
Electromagnetic Coupling ◽  
Random Noise ◽  
Horizontal Layer ◽  
Induced Polarization ◽  
Masking Effect ◽  
Synthetic Model ◽  
Survey Results ◽  
Agricultural Site ◽  
Noise Contamination

A novel technique is proposed to improve shallow induced polarization (IP) and resistivity survey results. We propose the apparent resistivity and apparent chargeability of second potential differences (SPD), employing two focused sources (FS) mathematically manipulated by superposition. To test the idea, a synthetic model is developed with two bodies. The first body is a small shallow heterogeneity which is above the second and larger body. This synthetic model illustrates the shape and response of the apparent resistivity and chargeability for FS under the random noise and masking effect. These processes reduce electromagnetic coupling, telluric noise, contamination in channel links, and small heterogeneous responses. A field test of the SPD for FS was carried out in an agricultural site irrigated with wastewater, where contaminated water laden with metals has been accumulating for years in the soil. Soil samples were collected and analyzed throughout the geophysical survey to correlate the resistivity-IP results. Soil laboratory analysis included metal content, moisture content and texture. The parameters computed after applying the SPD for FS depicted a better lateral resolution where vertical and horizontal boundaries of the anomaly zones were well defined. It was possible to determine the low permeability horizontal layer made of clay-soil (called tepetate), which is a barrier for water and metals.

scholarly journals Effects of shorter phase-resolved partial discharge duration on PD classification accuracy

2020 ◽  
Vol 11 (1) ◽  
pp. 326 ◽  
Author(s):  
Chong Wan Xin ◽  
Wong Jee Keen Raymond ◽  
Hazlee Azil Illias ◽  
Lai Weng Kin ◽  
Yiauw Kah Haur
Keyword(s):  
Pattern Recognition ◽  
Classification Accuracy ◽  
Random Noise ◽  
Partial Discharge ◽  
Measurement Data ◽  
Training Data ◽  
Support Vector ◽  
Discharge Duration ◽  
Artificial Neural Network Ann ◽  
Noise Contamination

<span>Partial discharge (PD) pattern recognition is useful to diagnose insulation condition. PD measurement data is commonly represented in phase-resolved partial discharge (PRPD) format. PRPD is useful as it provides a visible pattern for different PD source and various features can be extracted for PD pattern recognition. Shorter PRPD duration will enable more training data but the information in each data is less and vice versa. This works aims to investigate the effects of using very short duration PRPD data on the accuracy of PD pattern recognition. The results conclude that machine learning models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are robust enough such that reduction of PRPD duration from 15-seconds to 1-second causes less than 5 % drop in the classification accuracy. However, this is only true for noise free condition. <span>When the same PD data is overlapped with random noise, the classification accuracy suffers a significant reduction up to 19%. Therefore, longer PRPD duration is recommended to withstand the effects of noise contamination.</span></span>

Sign in / Sign up

Export Citation Format

Share Document