相关信号DOA估计的超分辨算法及其改进

常见的超分辨算法,如MUSIC算法、Capon算法等,只能对非相关信号到达角进行估计,无法分辨两个相关回波信号。介绍的d-MUSIC算法解决了这个问题,文中给出了这种算法的适用范围和空间谱特性,提出增加泰勒展开阶数的方法来提高算法的测角精度。

Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

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.

A NOVEL ALGORITHM OF SUPER-RESOLUTION RECONSTRUCTION FOR COMPRESSED VIDEO

Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS),this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS),a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR al-gorithm converges at a fast rate and obtains better performance in both objective and subjective quality,which is applicable for compressed video.

A NOVEL SUPER-RESOLUTION BEAMFORMING ALGORITHM

A novel simply-structured hybrid smart antenna system suitable to be used in ad-hoc network terminals is proposed in this letter. The super-resolution beamforming algorithm is also pre-sented based on the system using DOA estimation results. The algorithm can switch the beamforming to the direction of the expected signal and get the best transmitting performance after the pre-beamforming of the Butler matrix. The shifting value formulas are presented to obtain the best SNR when there is no interfering signal and to acquire the highest Signal to Interference Ratio (SIR) as there is one interfering signal. When there are more than one interfering signals,the pre-beamforming feature of the Butler matrix can also suppress the interfering signals. Simulation results verified the algorithm.

Research on the New Algorithm of Image Super Resolution Reconstruction

This paper put forward the super-resolution image algorithm based on Gauss process regression sparse solution. We establish local Gauss process regression model, to solve the feasibility problem of regression super-resolution problem in solving Gauss process; further use sparse algorithm, not only it can optimize the super parameter of Gauss kernel function, but also to optimize the initial entry training, so as to obtain more accurate regression Gauss process. Experimental results show that： the paper proposed algorithm can does not reduce the image reconstruction results, and it can reduce the computational complexity.

A super-resolution algorithm for synthetic aperture radar based on modified spatially variant apodization

The existing spatially variant apodizations(SVAs) either cannot depress the sidelobes effectively or reduce the energy of the mainlobe.To improve this,a modified SVA(MSVA) is put forward in this paper,which expands the traditional filter from 3-taps to 5-taps and sets relevant parameters according to different sampling rates to get the excellent result that satisfies constrained optimization theory.A modified super-SVA is also presented,which compares the result after the iteration with the original signal and makes the one whose amplitude is smaller as the initial value of the next iteration.This method can eliminate the sidelobes produced by the intermediate operation,so that the following bandwidth extrapolation is more available.Super-MSVA is presented based on the modified SVA and modified super-SVA,which is suitable for any Nyquist sampling rate,can extrapolate the signal bandwidth many times through iteration with a commensurate improvement in resolution,as demonstrated by the result of the experiment.

Computational methods in super-resolution microscopy

The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.

A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution

This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.