Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean imag...Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network （CNN） for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model （BMCNN） could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio （PSNR） when denoising images with high noise levels （σ 〉 40）, and the best visual quality when denoising images with all the tested noise levels.展开更多
Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connectio...Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connection between convolutional neural networks(CNNs)and Gabor filters.Therefore,some Gabor-based CNN methods have been proposed for HSI classification.However,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process.Moreover,these methods require patch cubes as network inputs.Such patch cubes may contain interference pixels,which will negatively affect the classification results.To address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI classification.More precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training process.Furthermore,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels problem.Experimental results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.展开更多
Modeling has become phenomenal in developing new products.In the case of filters,one of the mos applied procedures is via the construction of idealized physical computational models bearing close semblance to real fil...Modeling has become phenomenal in developing new products.In the case of filters,one of the mos applied procedures is via the construction of idealized physical computational models bearing close semblance to real filter media.It is upon these that multi-physics tools were applied to analvze the fow of fuid and the resulting typical performance parameters.In this work,two 3D filter membranes were constructed with MATLAB:one had a random distribution of unimodal nanofibers,and the other,a novel modification,formed.a bimodal distribution:both of them had similar dimensions and solid volume fractions.A comparison of their performance in a dust-loading environment was made by using computational fluid dynamic-discrete elemen method(CED-DEM)coupling technique in STAR-CCM+.It was found that the bimodal nanofiber membrane greatly improved the particle capture efficiencv.Whereas this increased the pressure drop,the gain was not toosignificant.Thus.overall,the results of the figure of merit ptoved that adopting a bimodal formation improved the filter's quality.展开更多
In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising methods have the potential ability to improve denoising p...In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time.Therefore,we propose a rapid post-processing method for Gaussian noise removal in this paper.Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise.Although these nonlocal image denoising methods have quantitatively high performance,some fine image details are lacking due to the loss of high frequency information.To tackle this problem,an improvement to the pioneering RAISR approach(rapid and accurate image super-resolution),is applied to rapidly post-process the denoised image.It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost,preserving important image structures well.Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements:geometric conversion and reduction of the strength classes.In addition,following RAISR,the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results.Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods,efficiently and with low memory requirements.展开更多
Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of tissues.The aim of our research is to design a new 3D algorithm using the Shifted Fourier Trans...Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of tissues.The aim of our research is to design a new 3D algorithm using the Shifted Fourier Transform(SFT)to perform a quasi-static elastography.Our innovative idea is implementation of a 3D convolution instead of using three 2D convulsions.At first,we collected the raw data from Abaqus engineering software in the form of breast tissue with a coefficient of elasticity of healthy tissue and tumor tissue with a coefficient of elasticity of tumor tissue.The primary raw data consists of a number of points with x,y and z specified for tumor and healthy breast tissue.At this step,we simulated the displacements in directions of x,y and z at each point of the prescribed tissues for 15 mm displacement of probe in–Y direction then we collected 1831 points for tumor and 4186 points for breast before and after pressure.After applying a novel reconstruction algorithm,we convolved all images with the 3D Gabor filters to obtain phases,represented displacements of the breast and tumor images for before and after pressure.To reach this goal,we designed a Gabor filter bank based on the dimensions of the input images in different scales,directions,and deviations.Using the 3D SFT,we calculated the displacements of the breast and tumor tissues followed by 3D elastogram representation of the images.Finally,we implemented a 2D analysis of SFT in order to investigate validation of the 3D SFT.In 2D algorithm,we used three two-dimensional convulsions in XY,YZ and XZ planes.The results obtained from the small displacements marked by circles,confirmed the accuracy of the 3D SFT algorithm.These areas of interest are the tumor areas in the 2D analysis.展开更多
基金This research was supported by the National Natural Science Foundation of China under Grant Nos. 61573380 and 61672542, and Fundamental Research Funds for the Central Universities of China under Grant No. 2016zzts055.
文摘Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network （CNN） for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model （BMCNN） could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio （PSNR） when denoising images with high noise levels （σ 〉 40）, and the best visual quality when denoising images with all the tested noise levels.
基金Project supported by the Fundamental Research Funds in Heilongjiang Provincial Universities(Grant No.145109218)the Natural Science Foundation of Heilongjiang Province of China(Grant No.LH2020F050)
文摘Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image processing.Recently,researchers have found a connection between convolutional neural networks(CNNs)and Gabor filters.Therefore,some Gabor-based CNN methods have been proposed for HSI classification.However,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process.Moreover,these methods require patch cubes as network inputs.Such patch cubes may contain interference pixels,which will negatively affect the classification results.To address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI classification.More precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training process.Furthermore,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels problem.Experimental results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.
基金the Chang Jiang Youth Scholars Program of China(No.51773037)the National Natural Science Foundation of China(Nos.51803023 and 61771123)+2 种基金the Shanghai Sailing Program(No.18YF1400400)the China Postdoctoral Science Foundation(No.2018M640317)the Fundamental Hesearch Funds for the Central Universities(No.2232018A3-11)。
文摘Modeling has become phenomenal in developing new products.In the case of filters,one of the mos applied procedures is via the construction of idealized physical computational models bearing close semblance to real filter media.It is upon these that multi-physics tools were applied to analvze the fow of fuid and the resulting typical performance parameters.In this work,two 3D filter membranes were constructed with MATLAB:one had a random distribution of unimodal nanofibers,and the other,a novel modification,formed.a bimodal distribution:both of them had similar dimensions and solid volume fractions.A comparison of their performance in a dust-loading environment was made by using computational fluid dynamic-discrete elemen method(CED-DEM)coupling technique in STAR-CCM+.It was found that the bimodal nanofiber membrane greatly improved the particle capture efficiencv.Whereas this increased the pressure drop,the gain was not toosignificant.Thus.overall,the results of the figure of merit ptoved that adopting a bimodal formation improved the filter's quality.
基金This research was funded by the National Natural Science Foundation of China under Grant Nos.61873117,62007017,61773244,61772253,and 61771231。
文摘In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time.Therefore,we propose a rapid post-processing method for Gaussian noise removal in this paper.Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise.Although these nonlocal image denoising methods have quantitatively high performance,some fine image details are lacking due to the loss of high frequency information.To tackle this problem,an improvement to the pioneering RAISR approach(rapid and accurate image super-resolution),is applied to rapidly post-process the denoised image.It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost,preserving important image structures well.Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements:geometric conversion and reduction of the strength classes.In addition,following RAISR,the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results.Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods,efficiently and with low memory requirements.
文摘Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of tissues.The aim of our research is to design a new 3D algorithm using the Shifted Fourier Transform(SFT)to perform a quasi-static elastography.Our innovative idea is implementation of a 3D convolution instead of using three 2D convulsions.At first,we collected the raw data from Abaqus engineering software in the form of breast tissue with a coefficient of elasticity of healthy tissue and tumor tissue with a coefficient of elasticity of tumor tissue.The primary raw data consists of a number of points with x,y and z specified for tumor and healthy breast tissue.At this step,we simulated the displacements in directions of x,y and z at each point of the prescribed tissues for 15 mm displacement of probe in–Y direction then we collected 1831 points for tumor and 4186 points for breast before and after pressure.After applying a novel reconstruction algorithm,we convolved all images with the 3D Gabor filters to obtain phases,represented displacements of the breast and tumor images for before and after pressure.To reach this goal,we designed a Gabor filter bank based on the dimensions of the input images in different scales,directions,and deviations.Using the 3D SFT,we calculated the displacements of the breast and tumor tissues followed by 3D elastogram representation of the images.Finally,we implemented a 2D analysis of SFT in order to investigate validation of the 3D SFT.In 2D algorithm,we used three two-dimensional convulsions in XY,YZ and XZ planes.The results obtained from the small displacements marked by circles,confirmed the accuracy of the 3D SFT algorithm.These areas of interest are the tumor areas in the 2D analysis.
