Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive p...Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive projected Landweber （APL） super-resolution algorithm using a spectral correction procedure, which attempts to combine the strong points of all of the projected Landweber （PL） iteration and the adaptive relaxation parameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are implemented as the main image restoration scheme and a spectral correction method is included in which the calculated spectrum within the passband is replaced by the known low frequency component. Then, the algorithm updates the relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed with simulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. From experiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution and has lower mean square error （MSE）. These constraints and adaptive character and spectral correction procedures speed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizing the image restoration problem.展开更多
Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model....Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced;the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.展开更多
Based on the mechanism of imagery, a novel method called the delaminating combining template method, used for the problem of super-resolution reconstruction from image sequence, is described in this paper. The combini...Based on the mechanism of imagery, a novel method called the delaminating combining template method, used for the problem of super-resolution reconstruction from image sequence, is described in this paper. The combining template method contains two steps： a delaminating strategy and a combining template algorithm. The delaminating strategy divides the original problem into several sub-problems; each of them is only eonnected to one degrading factor. The combining template algorithm is suggested to resolve each sub-problem. In addition, to verify the valid of the method, a new index called oriental entropy is presented. The results from the theoretical analysis and experiments illustrate that this method to be promising and efficient.展开更多
Aiming at solving the problem of low resolu- tion and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism （HVPM） was proposed. This method...Aiming at solving the problem of low resolu- tion and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism （HVPM） was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets （POCS） reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super- resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approxi- mately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.展开更多
The resolution of conventional light microscopy is insufficient for subcelluar studies.The invention of various super-resolution imaging techniques breaks the diffraction barrier and pushes the resolution limit toward...The resolution of conventional light microscopy is insufficient for subcelluar studies.The invention of various super-resolution imaging techniques breaks the diffraction barrier and pushes the resolution limit towards the nanometer scale.Here,we focus on a category of super-resolution microscopy that relies on the stochastic activation and precise localization of single molecules.A diversity of fluorescent probes with different characteristics has been developed to achieve super-resolution imaging.In addition,with the implementation of robust localization algorithms,this family of approaches has been expanded to multi-color,three-dimensional and live cell imaging,which provides a promising prospect in biological research.展开更多
基金the National Natural Science Foundation of China (60632020).
文摘Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive projected Landweber （APL） super-resolution algorithm using a spectral correction procedure, which attempts to combine the strong points of all of the projected Landweber （PL） iteration and the adaptive relaxation parameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are implemented as the main image restoration scheme and a spectral correction method is included in which the calculated spectrum within the passband is replaced by the known low frequency component. Then, the algorithm updates the relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed with simulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. From experiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution and has lower mean square error （MSE）. These constraints and adaptive character and spectral correction procedures speed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizing the image restoration problem.
文摘Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced;the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.
文摘Based on the mechanism of imagery, a novel method called the delaminating combining template method, used for the problem of super-resolution reconstruction from image sequence, is described in this paper. The combining template method contains two steps： a delaminating strategy and a combining template algorithm. The delaminating strategy divides the original problem into several sub-problems; each of them is only eonnected to one degrading factor. The combining template algorithm is suggested to resolve each sub-problem. In addition, to verify the valid of the method, a new index called oriental entropy is presented. The results from the theoretical analysis and experiments illustrate that this method to be promising and efficient.
文摘Aiming at solving the problem of low resolu- tion and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism （HVPM） was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets （POCS） reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super- resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approxi- mately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.
基金supported by the National Natural Science Foundation of China(31130065,31127002,31100615 and 31100596)the Chinese Academy of Sciences Project(yg2012029)
文摘The resolution of conventional light microscopy is insufficient for subcelluar studies.The invention of various super-resolution imaging techniques breaks the diffraction barrier and pushes the resolution limit towards the nanometer scale.Here,we focus on a category of super-resolution microscopy that relies on the stochastic activation and precise localization of single molecules.A diversity of fluorescent probes with different characteristics has been developed to achieve super-resolution imaging.In addition,with the implementation of robust localization algorithms,this family of approaches has been expanded to multi-color,three-dimensional and live cell imaging,which provides a promising prospect in biological research.
