融合梯度信息与HVS滤波器的无参考清晰度评价

目的 目前无参考图像质量评价算法的性能存在较大的提升空间,为了提高清晰度评价技术,提出了一种基于梯度信息与HVS滤波器的无参考清晰度评价算法(GI-F)。方法 该算法首先利用梯度算子计算各像素点的梯度信息,再通过HVS滤波器得到加权和作为图像的清晰度指标。结果 在公开数据库LIVE、TID2008和CSIQ上进行的实验,GI-F与S3(Spectral and Spatial Sharpness)、CPBD(Cumulative Probability of Blur Detection)和LPC-SI(Local Phase Coherence-based Sharpness Index)相比,性能指标RMSE(Root Mean Squared Error)、PLCC(Pearson Linear Correlation Coefficient)和SROCC(Spearman Rank-Order Correlation Coefficient)分别提升了20.66%、4.61%和3.33%;同时GI-F还具有更低的计算复杂度,即使与目前最好的BRISQUE(Blind/Referenceless Image Spatial QUality Evaluator)算法相比,耗时也降低了79.72%。结论 该算法只需耗费更少的时间即可计算出与人眼感知更加接近的客观清晰度指标,可广泛用于无参考图像情况下的清晰度指标计算场合,同时还可以通过并行计算进一步降低其计算时间。     

基于TS201系列DSP的PGA算法实现

相位梯度自聚焦(PGA)算法被广泛用于机载合成孔径雷达(SAR)成像中.由于其过程复杂,数据处理量大,为满足机载SAR实时处理的要求,选用具有强大处理能力的TS201 DSP.为了充分发挥TS201的性能,利用PGA原理和TS201处理器的特点,对算法流程进行优化,并且对最耗时的FFT采用了汇编语言实现,使得运算量和处...     

基于子孔径相位误差拼接的相位梯度自聚焦算法

随着雷达技术的发展,合成孔径雷达的成像精度也得到了大幅提升,与之相匹配的,也需要能更精确地进行运动补偿的算法.由于惯导精度无法满足需求,如果仅使用惯导数据对运动误差进行补偿,补偿的结果中含有残余的距离单元徙动,这会损失图像的精度.本文首先分析了残余的距离单元徙动对快速分解后向投影成像算法和相位梯度自聚焦算法的影响,再将...     

一种基于钟摆模型的舰船目标成像方法

舰船目标受海浪的影响而呈现多倍周期和随机性的摆动。针对这种情况,提出了一种基于钟摆模型的成像方法。该方法利用每个距离单元的相位历程中所含大幅度的线性调频分量进行成像。为了补偿实际目标转动过程中因不完全符合钟摆转动模型而引起的三阶及更高阶的相位误差,在一阶相位误差估计算法的基础上得出了二阶相位误差估计方法,并结合Radon-Wigner算法以及CLEAN法得出了比较稳健的相位自聚焦方法。实测数据处理结果证实了该方法的有效性。     

星载合成孔径雷达二维闪烁相位误差校正方法研究

分析了电离层闪烁效应对低频段星载合成孔径雷达(Synthetic Aperture Radar, SAR)方位向分辨率的影响, 并基于二维相位屏方法生成了距离向空变的闪烁相位误差.传统的相位梯度自聚焦(Phase Gradient Autofocus, PGA)方法难以适用于二维空变相位误差校正, 而本文基于闪烁相位误差在距离向具有连续性的特点, 在一定规则基础上将整幅图像划分为若干个子孔径, 对每个子孔径图像利用PGA方法进行相位误差估计, 再将得到的各子孔径相位误差进行插值运算, 从而得到整幅图像的闪烁相位误差.仿真结果表明:相比于传统PGA方法, 子孔径PGA方法可以有效解决二维空变闪烁相位误差对图像方位向分辨率的影响, 校正后的图像得到很好的恢复, 方位向分辨率明显提高.     

合成孔径雷达（SAR）的最新发展

全文分若干单元分别论述了机载2D-SAR自聚焦技术，降低斑点技术，MTI-SAR和3D-SAR（含机载和空载），对上述各种SAR分别讨论了工作原理，列出了多个实例以及应用情况。     

ISAR机动目标的平动补偿和瞬时成像研究

机动目标成像近年来受到广泛注意.本文首先讨论平动补偿,它通常可以分解为两步进行──包络对齐和自聚焦,分析表明,针对平稳目标的包络对齐方法仍然适用于机动目标,而根据相干积累原理,已有的自聚焦方法从理论上和实际上都不是最优的,我们提出适用于机动目标和平稳目标的迭代相干积累自聚焦(ICSA)算法,PGA(相位梯度算法)是ICSA算法的一个特例; 然后,本文讨论机动目标的瞬时成像,它实际上是一个瞬时谱估计问题,已有的一些瞬时成像方法只适用于散射点子回波为线性调频信号(多普勒分布为直线).针对时频分布为非直线的情况,我们提出用自适应窗短时chirplet分解方法估计信号的瞬时频率和瞬时幅度,并结合"洁净"技术,提出了快速自适应窗短时chirplet分解成像(ACDI)算法.实测数据的处理表明本文提出ICSA算法和ACDI算法是有效的.     

A novel autofocus algorithm based on maximum total variation criteria for SAR images

A novel autofocus algorithm for synthetic aperture radar (SAR) based on total variation is presented in this paper. The method, which starts with a complex phasedegraded SAR image, after the phase errors model is introduced into the range-compressed phase-history domain, carries out phase errors correction by changing the focus till the total variation of the azimuth profile is maximized. Compared with the minimum entropy autofocus algorithm, the autofocus algorithm has less computational complexity and is easier to implement. The simulation and the processing results of the measured data show the validity of the proposed method. Translated from Journal of Xidian University, 2006, 33(2): 167–172 [译自: 西安电子科技大学学报]     

The Weibull manifold in low-level image processing: An application to automatic image focusing

In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve low-level image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.     

Autofocusing for tissue microscopy

This paper describes the implementation of autofocusing for tissue microscopy. We first investigate the suitability of several criterion functions for the evaluation of image sharpness. Since tissue sections are invariably stained, we also discuss the selection of the colour component on which autofocusing will be performed. In tissue microscopy, where a section generally comprises multiple layers, it is often not possible to obtain an image that is well focused over the field of view because of the limited depth of field of the objective. We describe focus enhancement algorithms, closely related to the autofocus system, which may be employed to obtain an entirely sharp image.