Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients,as unimodal images provide limited valid information.To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information,a new multimodality medical image fusion method(NSST-PAPCNNLatLRR)is proposed in this paper.Firstly,the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST.Then,the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients.The improved PAPCNN model was based on the automatic setting of the parameters,and the optimal method was configured for the time decay factor ae.The experimental results show that,in comparison with the five mainstream fusion algorithms,the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images,and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in sixobjectiveindexes.

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones(TCs)precipitation(DSAEF_LTP)utilises an operational numerical weather prediction(NWP)model for the forecast track,while the precipitation forecast is obtained by finding analog cyclones,and making a precipitation forecast from an ensemble of the analogs.This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments.Experiments use four model versions.The control experiment DSAEF_LTP_1 includes three factors including TC track,landfall season,and TC intensity to determine analogs.Versions DSAEF_LTP_2,DSAEF_LTP_3,and DSAEF_LTP_4 respectively integrate improved similarity region,improved ensemble method,and improvements in both parameters.Results show that the DSAEF_LTP model with new values of similarity region and ensemble method(DSAEF_LTP_4)performs best in the simulation experiment,while the DSAEF_LTP model with new values only of ensemble method(DSAEF_LTP_3)performs best in the forecast experiment.The reason for the difference between simulation(training sample)and forecast(independent sample)may be that the proportion of TC with typical tracks(southeast to northwest movement or landfall over Southeast China)has changed significantly between samples.Forecast performance is compared with that of three global dynamical models(ECMWF,GRAPES,and GFS)and a regional dynamical model(SMS-WARMS).The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm.Compared with TCs without heavy precipitation or typical tracks,TCs with these characteristics are better forecasted by the DSAEF_LTP model.

Assessment and improvement of Noah-MP for simulating water and heat exchange over alpine grassland in growing season

Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction process on the TP.In this study,we assessed and improved the ensemble numerical simulations of the community Noah land surface model with multiparameterization options(Noah-MP)by using observations collected from four alpine grassland observation sites.The four observation sites belong to the upper Heihe River Basin Integrated Observatory Network located in the northeastern part of the TP.First,an ensemble of 1008 numerical simulation experiments,based on multiparameterization options of seven physical processes/variables in the Noah-MP,was carried out for the vegetation growing season.The Taylor skill score was then used to assess the model performance and select the optimal combination of parameterization options for a more exact simulation of the water and heat exchange in alpine grassland.The accuracy of Noah-MP simulation was further improved by introducing new parameterizations of thermal roughness length,soil hydraulic properties,and vertical root distribution.It was found that:(1)Simulation of water and heat exchange over alpine grassland in the growing season was mainly affected by the parameterizations of dynamic vegetation,canopy stomatal resistance,runoff and groundwater dynamics,and surface exchange coefficient for heat transfer.Selection of different parameterization options for these four physical processes/variables led to large differences in the simulation of water and heat fluxes.(2)The optimal combination of parameterization options selected in the current Noah-MP framework suffered from significant overestimation of sensible heat flux(H)and underestimation of soil moisture(θ)at all observation sites.(3)The overestimation of H was significantly improved by introducing a new parameterization of thermal roughness length.Furthermore,the underestimation ofθwas resolved by introducing a new parameterization of soil hydraulic properties that considered the organic matter effect and a new vertical distribution function for the vegetation root system.The results of this study provide an important reference for further improving the simulation of water and heat exchange by using the land surface model in alpine grassland.