Several studies of natural-image statistics have concluded that the majority of structural image information is represented in the Fourier domain not by the power spectrum, which can be modeled with a power function of spatial frequency, but by the phase spectrum. Psychophysical experiments are described in which human observers were required to discriminate between visual stimuli differing only in their global second-order statistics. The patterns of sensitivity recorded were found to depend on whether these stimuli were natural images or images with random-phase spectra. These results suggest that discrimination performance is related to changes in higher-order image structure; a bandpass model observer based on third-moment statistics was applied to all stimulus images and was found to provide a good fit to the psychophysical data.

/pdf/role-of-second-and-third-order-statistics-in-the-ri7iw13ski.pdf

Role of second- and third-order statistics in the discriminability of natural images

The idea of asymmetric windowing as a way of reducing time delay in spectral analysis is explored. Traditional windows are analyzed in relation to time delay. Some asymmetric windows are introduced and a comparative analysis with traditional windows is made. It is shown that it is possible to reduce the time delay up to about 25% of the analysis window size. Application to linear predictive speech coding is analyzed, and the experimental results of using an asymmetric window on a standard LPC vocoder are presented. >

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/AsymWindows_icassp1991.pdf

On the use of asymmetric windows for reducing the time delay in real-time spectral analysis

In the field of sensor signal processing, windows are time-/frequency-domain weighting functions that are widely applied to reduce the well-known Gibbs oscillations Conventional methods generally control the spectral characteristics of windows by adjusting several of the parameters of closed-form expressions Designers must make trade-offs among the mainlobe width (MW), the peak sidelobe level (PSL), and sometimes the sidelobe fall-off rate (SLFOR) of windows by carefully adjusting these parameters Generally, not all sidelobes need to be suppressed in specified applications In this paper, a novel method, ie, the inverse of the shaped output using the cyclic algorithm (ISO-CA), for designing window functions with flexible spectral characteristics is proposed Simulations are conducted to test the effectiveness, flexibility and versatility of the method Some experiments based on real measured data are also presented to demonstrate the practicability The results show that the window functions generated using the cyclic algorithm (CA) yield better performance overall than the windows of conventional methods, achieving a narrower MW, a lower PSL, and a controllable SLFOR In addition, steerable sidelobes over specified regions can be acquired both easily and flexibly while maintaining the original properties of the initial window as much as possible

A Novel Method for Designing General Window Functions with Flexible Spectral Characteristics.

This paper provides a complete method to solve the defects of spectrum analysis for discrete signals. Many researches had issued to deal with the defects of fast Fourier transform (FFT). Those methods may cause the characteristics of original signal altered, or solve this problem in part only. The defects of spectrum analysis cannot be solved efficiently and completely by those methods. These defects result from that those frequency scales cannot match with signal characteristics. This paper is based on the concept to establish a complete solution, divided into three steps: First, this paper analyzes the signal characteristics to be the basis of spectrum adjustment. A simple and accurate algorithm is used to find the frequency and amplitude of each component. Next, this paper finds optimum spectrum parameters to make the new spectrum match with signal characteristics. Finally, this paper takes the parameters to reanalyze the original signal. The method will make spectrum analysis reach optimization. Every procedure in this paper compares with traditional ones to prove its benefits. Moreover, the authors verify the theory feasible by analyzing actual signals.

The optimization of spectrum analysis for digital signals

This paper shows a fast implementation method of a two dimensional (2D) filter. The filter design is based on fast convolution related to the fast Fourier transform (FFT) look up table. This is then extended to the 2D FFT. The design and implementation of the system gives a good accurate selection of the desired frequency. The system has a step advantage of a reduction in the operation time. >

Fast 2D convolution filter based on look up table FFT

Two different window function families, namely, the first-order Bessel (I/sub 1/-cosh) family and raised-cosine family, which have variable parameters and hence make them flexible in digital spectrum analysis applications, are considered. Closed-form expressions are obtained which facilitate the tradeoffs between record length, spectral resolution, leakage suppression, bandwidth, etc. Simple expressions relating to mainlobe width and maximum sidelobe level are given for the two families considered. The results are compared to those obtained by J.F. Kaiser and R.W. Schafer (1980) in the case of zeroth-order Bessel (I/sub 0/-sinh) family. >

Variable parameter window families for digital spectral analysis

An analysis of a digital moving target indicator (DMTI) using the traditional fast Fourier transform (FFT) and the recently developed nonlinear spectral estimation techniques is presented. The performance evaluation has been done using simulated target and clutter models in the presence of white Gaussian noise. MTI improvement factor has been computed for all the cases, and it clearly shows the superiority of the nonlinear spectral estimation methods, especially for short data lengths. >

KBhoopathy Bagan

Papers

Variable parameter window families for digital spectral analysis

Computer simulation study of MTI using FFT and nonlinear processors