Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motiva...Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.展开更多
OBJECTIVE： To explore the relationship between Renying pulse （carotid） augmentation index （AI） and Cunkou pulse condition in different blood pres- sure groups, and the clinical significance of Reny- ing and Cunko...OBJECTIVE： To explore the relationship between Renying pulse （carotid） augmentation index （AI） and Cunkou pulse condition in different blood pres- sure groups, and the clinical significance of Reny- ing and Cunkou pulse parameters to reflect vascu- lar function. METHODS： Eighty-six patients with essential hyper- tension （EH） and 52 individuals with normal blood pressure （control group） between and January 2012 were included September 2010 this study. Reny- ing pulse AI was examined by a new diagnostic tool （ALOKA ProSound Alpha 10） --wave intensity （Wl） that is calculated as the product of the deriva- tives of the simultaneously recorded blood pres- sure changes （dP/dt） and blood-flow-velocity changes （dU/dt）, while Cunkou pulse condition was detected by DDMX-100 Pulse Apparatus inboth EH and control groups. A multifactorial corre- lation analysis was performed for data analysis. RESULTS： After adjusting for potential confound- ing variables, in the EH group, AI was positively cor- related with ts, w2/t （rts=0.225, P〈0.05; rw2/t=0.230, P〈 0.05） and negatively correlated with hs, hs/hl and w2 （rhs=- 0.393,P〈0.01 ;rhs/l=- 0.444, P〈0.01 ;rw2=- 0.389, P〈0.01）. In the control group, AI was positively cor- related with t3, t4, ts and w, （rt3=0.595, P〈0.01; r,4= 0.292, P〈0.05; rt5=0.318, P〈0.05; rw1=0.541, P〈0.01） and negatively correlated with h1, h2, h3, Ad and A （rh1= - 0.368, P〈0.05; rh2= - 0.330, P〈0.05; rh3= - 0.327, P〈 0.05; rAd=- 0.322, P〈0.05; rA=- 0.410, P〈0.01）. In the total sample group （EH plus control group, n= 138）, AI was positively correlated with t, ts, w1 and w, （rt=0.257, P〈0.01; rt5=0.266, P〈0.01; rw1=0.184, P〈 0.05; rw/t=0.210, P〈0.05） and negatively correlated with hs, hs/hl, w2 and Ad （rhs= - 0.230, P〈0.01; rh5/h1= - 0.218, P〈0.05; rw2= - 0.267, P〈0.01; rAd= - 0.246, P〈0.01）. Multiple linear regression analysis was car- ried out to model the relationship （F=7.887, P〈 0.001）.CONCLUSION： Renying pulse AI can effectively pre- dict arterial stiffness in synchrony with the manifes- tations of Cunkou pulse in elderly patients with hy- pertension. Cunkou pulse apparatus is a valuable tool for evaluating AI in clinical practice, The close correlations reported above reflect the holistic con- cept of Traditional Chinese Medicine.展开更多
Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers.Due to the lack of professional knowledge,the quality of crowdsourced labels is relatively low.A common approach to addressing ...Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers.Due to the lack of professional knowledge,the quality of crowdsourced labels is relatively low.A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true label.However,to our knowledge,almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each instance.To solve these issues,this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration(AALI).In the first stage,we design an attribute augmentation method to enrich the original attribute space.In the second stage,we develop a filter to single out reliable instances with high-quality multiple noisy label sets.In the third stage,we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all instances.Experimental results on simulated and real-world crowdsourced datasets demonstrate that AALI outperforms all the other stateof-the-art competitors.展开更多
文摘Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.
基金Supportedby the Science andTechnology Project of Fujian Province(No.2014Y0007)the Fujian Province Medical Innovation Foundation(No.2009-CXB-13)
文摘OBJECTIVE： To explore the relationship between Renying pulse （carotid） augmentation index （AI） and Cunkou pulse condition in different blood pres- sure groups, and the clinical significance of Reny- ing and Cunkou pulse parameters to reflect vascu- lar function. METHODS： Eighty-six patients with essential hyper- tension （EH） and 52 individuals with normal blood pressure （control group） between and January 2012 were included September 2010 this study. Reny- ing pulse AI was examined by a new diagnostic tool （ALOKA ProSound Alpha 10） --wave intensity （Wl） that is calculated as the product of the deriva- tives of the simultaneously recorded blood pres- sure changes （dP/dt） and blood-flow-velocity changes （dU/dt）, while Cunkou pulse condition was detected by DDMX-100 Pulse Apparatus inboth EH and control groups. A multifactorial corre- lation analysis was performed for data analysis. RESULTS： After adjusting for potential confound- ing variables, in the EH group, AI was positively cor- related with ts, w2/t （rts=0.225, P〈0.05; rw2/t=0.230, P〈 0.05） and negatively correlated with hs, hs/hl and w2 （rhs=- 0.393,P〈0.01 ;rhs/l=- 0.444, P〈0.01 ;rw2=- 0.389, P〈0.01）. In the control group, AI was positively cor- related with t3, t4, ts and w, （rt3=0.595, P〈0.01; r,4= 0.292, P〈0.05; rt5=0.318, P〈0.05; rw1=0.541, P〈0.01） and negatively correlated with h1, h2, h3, Ad and A （rh1= - 0.368, P〈0.05; rh2= - 0.330, P〈0.05; rh3= - 0.327, P〈 0.05; rAd=- 0.322, P〈0.05; rA=- 0.410, P〈0.01）. In the total sample group （EH plus control group, n= 138）, AI was positively correlated with t, ts, w1 and w, （rt=0.257, P〈0.01; rt5=0.266, P〈0.01; rw1=0.184, P〈 0.05; rw/t=0.210, P〈0.05） and negatively correlated with hs, hs/hl, w2 and Ad （rhs= - 0.230, P〈0.01; rh5/h1= - 0.218, P〈0.05; rw2= - 0.267, P〈0.01; rAd= - 0.246, P〈0.01）. Multiple linear regression analysis was car- ried out to model the relationship （F=7.887, P〈 0.001）.CONCLUSION： Renying pulse AI can effectively pre- dict arterial stiffness in synchrony with the manifes- tations of Cunkou pulse in elderly patients with hy- pertension. Cunkou pulse apparatus is a valuable tool for evaluating AI in clinical practice, The close correlations reported above reflect the holistic con- cept of Traditional Chinese Medicine.
基金supported by the Science and Technology Project of Hubei Province-Unveiling System(2021BEC007)the Industry-University-Research Innovation Funds for Chinese Universities(2020ITA05008).
文摘Crowdsourcing provides an effective and low-cost way to collect labels from crowd workers.Due to the lack of professional knowledge,the quality of crowdsourced labels is relatively low.A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true label.However,to our knowledge,almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each instance.To solve these issues,this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration(AALI).In the first stage,we design an attribute augmentation method to enrich the original attribute space.In the second stage,we develop a filter to single out reliable instances with high-quality multiple noisy label sets.In the third stage,we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all instances.Experimental results on simulated and real-world crowdsourced datasets demonstrate that AALI outperforms all the other stateof-the-art competitors.
