Image processing using a reconfigurable platform: Pre-processing block hardware architecture

<p class="CM12">Real time image processing is a challenging task in which fetching the sub image requires offset memory access apart from core processing needs. This paper aims at overcoming the offset needs for memory addressing in pre-processing blocks. Another feature of this present work is to appending the image data with customized algorithmic reequipments viz duplicating, zero padding. For KxK kernel size, the proposed hardware architecture can be programmed to fetch K pixels in one cycle, reducing the data access time. Results have been compared with software-based processing for KxK spatial filtering. performance indicates significant timing improvement using proposed pre-processing hardware block.</p>

High Speed Maneuvering Platform Squint TOPS SAR Imaging Based on Local Polar Coordinate and Angular Division

This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is calculated in local polar format. The Doppler ambiguity is resolved by nonlinear derotation and zero-padding. The recovered signal is divided into several blocks in Doppler according to the angular division. Keystone transform is used to remove the space-variant range cell migration (RCM) components. Thus, the residual RCM terms can be compensated by a unified phase function. Frequency domain perturbation terms are introduced to correct the space-variant Doppler chirp rate term. The focusing parameters are calculated according to the scene center of each angular block and the signal of each block can be processed in parallel. The image of each block is focused in range-Doppler domain. After the geometric correction, the final focused image can be obtained by directly combined the images of all angular blocks. Simulated SAR data has verified the effectiveness of the proposed algorithm.

A Method for Improving the Accuracy of Natural Frequency Measurement Using In-the-loop Computing

The method presented in the paper is based on in-the-loop computing applied for impulse response to obtain a spectrum with a much higher frequency resolution than using FFT. Then, higher spectrum frequency resolution results in greater accuracy in estimation of natural frequencies. The frequency resolution of estimated spectrum in this method is completely independent of the length of impulse response and, by extension, the method eliminates the problem of spectral resolution limitation using FFT due to finite length of recorded signals. This fact is very useful and is the main advantage of the proposed method. The results of the method have been shown and compared in quantitative terms to natural frequencies estimated using classical FFT with zero-padding as reference method.

Optimization of Virtual Shack-Hartmann Wavefront Sensing

Virtual Shack–Hartmann wavefront sensing (vSHWS) can flexibly adjust parameters to meet different requirements without changing the system, and it is a promising means for aberration measurement. However, how to optimize its parameters to achieve the best performance is rarely discussed. In this work, the data processing procedure and methods of vSHWS were demonstrated by using a set of normal human ocular aberrations as an example. The shapes (round and square) of a virtual lenslet, the zero-padding of the sub-aperture electric field, sub-aperture number, as well as the sequences (before and after diffraction calculation), algorithms, and interval of data interpolation, were analyzed to find the optimal configuration. The effect of the above optimizations on its anti-noise performance was also studied. The Zernike coefficient errors and the root mean square of the wavefront error between the reconstructed and preset wavefronts were used for performance evaluation. The performance of the optimized vSHWS could be significantly improved compared to that of a non-optimized one, which was also verified with 20 sets of clinical human ocular aberrations. This work makes the vSHWS’s implementation clearer, and the optimization methods and the obtained results are of great significance for its applications.

Random-Walk Laplacian for Frequency Analysis in Periodic Graphs

This paper presents the benefits of using the random-walk normalized Laplacian matrix as a graph-shift operator and defines the frequencies of a graph by the eigenvalues of this matrix. A criterion to order these frequencies is proposed based on the Euclidean distance between a graph signal and its shifted version with the transition matrix as shift operator. Further, the frequencies of a periodic graph built through the repeated concatenation of a basic graph are studied. We show that when a graph is replicated, the graph frequency domain is interpolated by an upsampling factor equal to the number of replicas of the basic graph, similarly to the effect of zero-padding in digital signal processing.