The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothi...The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average, center of gravity, least squares of polynomial, slide converter of discrete funcion convolution etc. The process of spectrum data is realized, and the results are assessed in H/FWHM（ Peak High/Full Width at Half Maximum） and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion, but the Gaussian function theory in discrete function convolution slide method is used to filter the complex y-spectrum on Embedded system nlatform, and the statistical fluctuation of y-snectrum filtered wall.展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
The analysis of underwater topography ultrasonic image, which can be obtained by using B-scan ultrasound imaging technique, is the basis and important work in topography study. In this paper, we present a novel self-a...The analysis of underwater topography ultrasonic image, which can be obtained by using B-scan ultrasound imaging technique, is the basis and important work in topography study. In this paper, we present a novel self-adapted method for the extraction of submerged topographic line. Since the distribution of gray values in the image has a mutation process, this feature is used to appropriately track the topographic line of imaging band, and wavelet denoising method is applied to denoise the obtained lines. The described method also takes the continuity of topography into consideration during tracking procedure. The results show that the extraction error is within 2-pixel width（approximate 1 mm）. This method is suitable for the extraction of current model topographic lines with the advantages of good self-adaption, high speed and high resolution.展开更多
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
基金Sponsored by the Natural Science Fundation of Jiangxi Province(Grant No.20114BAB211026 and 20122BAB201028)the Open Science Fund from Key Laboratory of Radioactive Geology and Exploration Technology Fundamental Science for National Defense,East China Institute of Technology(Grant No.2010RGET11)
文摘The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average, center of gravity, least squares of polynomial, slide converter of discrete funcion convolution etc. The process of spectrum data is realized, and the results are assessed in H/FWHM（ Peak High/Full Width at Half Maximum） and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion, but the Gaussian function theory in discrete function convolution slide method is used to filter the complex y-spectrum on Embedded system nlatform, and the statistical fluctuation of y-snectrum filtered wall.
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
基金Supported by the Fundamental Research Funds for the Central Universities(2014212020205)
文摘The analysis of underwater topography ultrasonic image, which can be obtained by using B-scan ultrasound imaging technique, is the basis and important work in topography study. In this paper, we present a novel self-adapted method for the extraction of submerged topographic line. Since the distribution of gray values in the image has a mutation process, this feature is used to appropriately track the topographic line of imaging band, and wavelet denoising method is applied to denoise the obtained lines. The described method also takes the continuity of topography into consideration during tracking procedure. The results show that the extraction error is within 2-pixel width（approximate 1 mm）. This method is suitable for the extraction of current model topographic lines with the advantages of good self-adaption, high speed and high resolution.
