scholarly journals Using Color Difference Compensation Method to Balance and Repair the Image of Art Design

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Ji ◽  
Yang Shang
Keyword(s):  
Feature Fusion ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Chromatic Aberration ◽  
Color Difference ◽  
Wavelet Packet Decomposition ◽  
Image Block ◽  
White Balance ◽  
Matching Cost ◽  
Art Design

In the case of complex illumination background, the image has fuzziness and chromatic aberration, which can improve the imaging effect of the image by white balance chromatic aberration compensation. The traditional method uses the contour feature fusion adaptive matching method to compensate the white balance under the complex illumination background, which leads to the good color difference compensation effect when the image pixel is low. A white balance color compensation algorithm for fuzzy chromatic aberration based on wavelet packet decomposition is proposed. The original image was denoised and filtered. The white balance characteristics of the image were analyzed and extracted based on the wavelet packet decomposition method, and the adaptive balance design was carried out to realize the white balance and color difference compensation of the art design chromatic aberration image. By using the known pixel information of the image block to be repaired, the statistical properties of the image block to be repaired are predicted, and the matching cost of the image block to be matched that meets the restriction conditions is calculated. By introducing the objective factor, the matching cost function is improved to balance the restoration process, and the unnatural problem caused by the repeated appearance of some image details in the restoration results is solved. The simulation results show that the method can effectively balance the color difference of the image, improve the aesthetic feeling of the image, and improve the performance of the detail features of the image. The peak signal-to-noise ratio is high.

Calculation Analysis on Audio Frequency Compression Based on Wavelet Packet Decomposition

2012 ◽  
Vol 182-183 ◽  
pp. 2035-2039
Author(s):  
Kai Wen Zhang ◽  
Yan Ling Zhou ◽  
Xiang Gang Hua
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Audio Signal ◽  
Wavelet Packet Decomposition ◽  
Audio Frequency ◽  
Simulation Test ◽  
Signal To Noise ◽  
Adaptive Wavelet ◽  
Frequency Compression ◽  
Calculation Analysis

This text have further discussed the audio frequency compression based on wavelet packet decomposition and optimize the choice of optimum wavelet packet and proposed a optimized self-adaptive wavelet packet decomposition and adopted zerotree coding based on psycho-acoustic model, which makes this system have a higher compression ratio than audio signal and can keep good real-time and objective recovery signal to noise ratio. Computer simulation test is made for the calculation proposed with Matlab,and this simulation test has proved the point that is mentioned in this text.

scholarly journals Multilinear principal component analysis for iris biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1458
Author(s):  
Chetana Kamlaskar ◽  
Aditya Abhyankar
Keyword(s):  
Principal Component Analysis ◽  
Iris Recognition ◽  
Feature Fusion ◽  
Wavelet Packet ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Good Time ◽  
Wavelet Packet Decomposition ◽  
Multilinear Principal Component Analysis

<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to extract the iris region. In the second step, wavelet packet decomposition is conducted to extract features of the iris image, since good time and frequency resolutions can be provided simultaneously by the wavelet packet decomposition. In the next step, all decomposed nodes or packets are arranged as a 3<sup>rd</sup> order tensor rather than a long vector, in which feature fusion is directly implemented with multilinear principal component analysis (MPCA). This approach provides a more compact or useful low-dimensional representation directly from the original tensorial representation. Finally, a discriminative tensor feature selection mechanism and classification strategy are applied to iris recognition problem. The obtained results indicate the usefulness of MPCA to select discriminative features and fuse them effectively. The experimental results reveal that the proposed tensor-based MPCA approach achieved a competitive matching performance on the SDUMLA-HMT Iris database with an adequate acceptable rate.</p>

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

scholarly journals Adaptive frequency scaled wavelet packet decomposition for frog call classification

2016 ◽  
Vol 32 ◽  
pp. 134-144 ◽  
Author(s):  
Jie Xie ◽  
Michael Towsey ◽  
Jinglan Zhang ◽  
Paul Roe
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Call Classification

Application of CIEDE2000 Color Difference Formula in Jet Fuel Silver Strip Corrosion Color Recognition

2012 ◽  
Vol 531-532 ◽  
pp. 54-57 ◽  
Author(s):  
Li Ping Tong ◽  
Bin Peng ◽  
Yi Wei Fei ◽  
Hong Wei Yang
Keyword(s):  
Chromatic Aberration ◽  
Color Difference ◽  
Jet Fuel ◽  
Color Recognition ◽  
Difference Formula ◽  
Human Eyes

In order to eliminate the human eyes’ subjective differences in the level recognition of the jet fuel’s silver corrosion, chromatic aberration system is used to calculate chromatic aberration between the image of the standard colorimetric board and the experiment silver corrosion. By contrast, this paper chooses the least chromatic aberration to estimate the level of silver corrosion. The CIEDE2000 of the color difference formula, which can match with person vision theoretically, is applied in the chromatic aberration system at present, and can reflect the minor differences in color correctly. so this paper attempts to use CIEDE2000 color formula to determine the silver strip corrosion, which can reduce the influence of professionals’ subjective factors, and these results are more objective and accurate, which proves the actual feasibility of this method.

CONSTRUCTING MULTIPLE WAVELET PACKETS WITH FIFS

Fractals ◽  
2001 ◽  
Vol 09 (02) ◽  
pp. 165-169
Author(s):  
GANG CHEN ◽  
ZHIGANG FENG
Keyword(s):  
Multiresolution Analysis ◽  
Wavelet Packet ◽  
Wavelet Packets ◽  
Scaling Functions ◽  
Wavelet Packet Decomposition ◽  
Fractal Interpolation ◽  
Fractal Interpolation Functions ◽  
Interpolation Functions

By using fractal interpolation functions (FIF), a family of multiple wavelet packets is constructed in this paper. The first part of the paper deals with the equidistant fractal interpolation on interval [0, 1]; next, the proof that scaling functions ϕ1, ϕ2,…,ϕr constructed with FIF can generate a multiresolution analysis of L2(R) is shown; finally, the direct wavelet and wavelet packet decomposition in L2(R) are given.

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