A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. F...A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.展开更多
文摘目的采用点扩散函数(point spread function,PSF)分析系统,获得在人眼的视网膜成像质量表达中的各种特征,研究其在人眼成像质量评价中的作用。方法应用点扩散函数分析系统(PSF-1000),测量22例近视无散光者的视网膜成像质量,在瞳孔3mm和6mm的状态下,取22眼调制传递函数(modulation transfer function,MTF)曲线中的12个点,分别等同于对数视力表4.0～5.1的12个点,针对PSF分析系统中的MTF参数进行比较和统计学分析。结果①PSF反映的是光强度大小和位置的偏差。PSF分析系统可直接采用屈光系统MTF曲线表达人眼的成像特征,该曲线MTF值从低频至中频迅速下降,高频趋向缓和至零,定量并客观地表达了人眼成像质量从低频至高频的改变。②左、右眼的MTF表达具有镜像对称性。③个体之间MTF值差异大(如空间频率为3.00c/d时,3mm瞳孔直径的MTF值为0.68～0.98,6mm瞳孔直径的MTF值为0.51～0.85)。④3mm和6mm瞳孔直径时的MTF值差异有统计学意义(P<0.01)。结论点扩散函数分析系统既能表达总眼球成像的各种光学特性,又体现了像差、衍射和散射的共同影响。
基金Project(2008041001) supported by the Academician Foundation of China Project(N0601-041) supported by the General Armament Department Science Foundation of China
文摘A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.
