A subspace matching color filter design methodology for a multispectral imaging system.

In this paper, we present a methodology to design filters for an imaging system to improve the accuracy of the spectral measurements for families of reflective surfaces. We derive the necessary and sufficient conditions that the sensor space of the system must obey in order to measure the spectral reflectance of the surfaces accurately. Through simulations, we show how these conditions can be applied to design filters using a set of sample spectral data acquired from extracted teeth. For this set of data, we also compare our results to those of Wolski's method, a conventional filter design method which produces filters that recover tristimulus values of surfaces accurately under several illuminants. We show that our method produces filters that capture the spectral reflectance better given the same number of measurements. The errors in predicting the color of the sample data are much lower under every test illuminant when the filters designed with our method are used.     

Mathematical methods for the design of color scanning filters

The problem of the design of color scanning filters is addressed in this paper. The problem is posed within the framework of the vector space approach to color systems. The measure of the goodness of a set of color scanning filters presented in earlier work is used as an optimization criterion to design color scanning filters modeled in terms of known, smooth, nonnegative functions. The best filters are then trimmed using the gradient of the mean square DeltaE(ab) error to obtain filters with a lower value of perceptual error. The results obtained demonstrate the utility of the method.     

Filter considerations in color correction

The quality of color correction is dependent on the filters used to scan the image. This paper introduces a method of selecting the color filters using a priori information about the viewing illuminants. Color correction results using the derived filters are compared with color correction results using filters that are optimal for individual viewing and recording illuminants. The comparison is performed using the CIE PEL (L*a*b*) perceptual color difference measure. Applications of this work are found in the design of scanners, copiers, and television systems.     

Mathematical methods for the analysis of color scanning filters

The problem of the sensitivity analysis of color scanning filters is addressed in this paper. The second differential of the mean square DeltaE(ab) error provides a means of calculating the sensitivity of the mean square DeltaE(ab) error to filter fabrication errors. Tolerances on the allowable change in the mean square DeltaE(ab) error are used to define bounds on the filter fabrication errors at all wavelengths and at single wavelengths.     

Novel Color-Sequential Transflective Liquid Crystal Displays

A novel transflective liquid crystal display architecture and its system driving schemes are proposed. In the reflective mode, the ambient light is used to readout the displayed images. While in the transmissive mode, a color-sequential light emitting diode backlight is used to eliminate the color filters. Under such device configuration, several advantages such as increased brightness and maximized color saturation for both transmissive and reflective modes can be achieved     

Wavelet filter evaluation for image compression

Choice of filter bank in wavelet compression is a critical issue that affects image quality as well as system design. Although regularity is sometimes used in filter evaluation, its success at predicting compression performance is only partial. A more reliable evaluation can be obtained by considering an L-level synthesis/analysis system as a single-input, single-output, linear shift-variant system with a response that varies according to the input location module (2(L),2(L)). By characterizing a filter bank according to its impulse response and step response in addition to regularity, we obtain reliable and relevant (for image coding) filter evaluation metrics. Using this approach, we have evaluated all possible reasonably short (less than 36 taps in the synthesis/analysis pair) minimum-order biorthogonal wavelet filter banks. Of this group of over 4300 candidate filter banks, we have selected and present here the filters best suited to image compression. While some of these filters have been published previously, others are new and have properties that make them attractive in system design.     

Perceptually uniform color spaces for color texture analysis: anempirical evaluation

RGB, a nonuniform color space, is almost universally accepted by the image processing community as the means for representing color. On the other hand, perceptually uniform spaces, such as L*a*b*, as well as approximately-uniform color spaces, such as HSV, exist, in which measured color differences are proportional to the human perception of such differences. This paper compares RGB with L*a*b* and HSV in terms of their effectiveness in color texture analysis. There has been a limited but increasing amount of work on the color aspects of textured images. The results have shown that incorporating color into a texture analysis and recognition scheme can be very important and beneficial. The presented methodology uses a family of Gabor filters specially tuned to measure specific orientations and sizes within each color texture. Effectiveness is measured by the classification performance of each color space, as well as by classifier-independent measures. Experimental results are obtained with a variety of color texture Images. Perceptually uniform spaces are shown to outperform RGB in many cases     

Subpixel rendering for diamond-shaped PenTile displays using patch-based adaptive filters

We propose a novel subpixel rendering algorithm for diamond-shaped PenTile displays, which reduces color distortions while improving apparent resolutions. We develop two types of subpixel rendering filters: main filter and color distortion reduction (CDR) filters. To derive the filters, we formulate a quadratic program to minimize the difference between an original input image and a virtual image that the human visual system perceives. By imposing two constraints for filter size and coefficients, we obtain the main filter, which has a suitable size and is normalized. Then, we design the CDR filters based on the analysis of various patch patterns for image areas. We define the patch patterns to classify local areas with possible color distortions. By imposing additional constraints according to the patch patterns, we derive the CDR filters. Lastly, by matching local areas in the input image into the pre-defined patch patterns, we render the image using the main filter and the CDR filters, which are applied adaptively to the local areas. Experimental results demonstrate that the proposed subpixel rendering algorithm improves apparent resolutions and suppresses color distortions effectively, thereby outperforming conventional algorithms.     

Design and performance of adaptive systems based on structured stochastic optimization strategies

The theory and design of linear adaptive filters based on FIR filter structures is well developed and widely applied in practice. However, the same is not true for more general classes of adaptive systems such as linear infinite impulse response adaptive filters (MR) and nonlinear adaptive systems. This situation results because both linear IIR structures and nonlinear structures tend to produce multi-modal error surfaces for which stochastic gradient optimization strategies may fail to reach the global minimum. After briefly discussing the state of the art in linear adaptive filtering, the attention of this paper is turned to MR and nonlinear adaptive systems for potential use in echo cancellation, channel equalization, acoustic channel modeling, nonlinear prediction, and nonlinear system identification. Structured stochastic optimization algorithms that are effective on multimodal error surfaces are then introduced, with particular attention to the particle swarm optimization (PSO) technique. The PSO algorithm is demonstrated on some representative IIR and nonlinear filter structures, and both performance and computational complexity are analyzed for these types of nonlinear systems.     

L(p) norm design of stack filters.

Addresses the problem of designing optimal stack filters by employing an L(p) norm of the error between the desired signal and the estimated one. It is shown that the L(p) norm can be expressed as a linear function of the decision errors at the binary levels of the filter. Thus, an L(p)-optimal stack filter can be determined as the solution of a linear program. The conventional design of using the mean absolute error (MAE), therefore, becomes a special ease of the general L(p) norm-based design developed here. Other special cases of the proposed approach, of particular interest in signal processing, are the problems of optimal mean square error (p=2) and minimax (p-->infinity) stack filtering. Since an Linfinity optimization is a combinatorial problem, with its complexity increasing faster than exponentially with the filter size, the proposed L(p ) norm approach to stack filter design offers an additional benefit of a sound mathematical framework to obtain a practical engineering approximation to the solution of the minimax optimization problem. The conventional MAE design of an important subclass of stack filters, the weighted order statistic filters, is also extended to the L(p) norm-based design. By considering a typical application of restoring images corrupted with impulsive noise, several design examples are presented, to illustrate the performance of the L(p)-optimal stack filters with different values of p. Simulation results show that the L(p)-optimal stack filters with p=/>2 provide a better performance in terms of their capability in removing impulsive noise, compared to that achieved by using the conventional minimum MAE stack filters.     

基于Harris Hawks优化算法的介质波导滤波器优化设计

Harris Hawks优化(Harris Hawks optimization, HHO)算法是一种模拟鸟群合作捕食行为的新型群智能算法. 介质波导滤波器是当前5G移动通信设备急需的器件,因此如何利用新型优化算法高效且精确地对介质波导滤波器进行优化设计十分重要. 文中首先描述了HHO算法流程,并结合滤波器优化问题提出了一种通用框架;然后基于稳态假设对HHO算法的更新方程进行了理论分析,依据所导出的方程分析了算法的动态特性及收敛行为;最后利用HHO算法实现了两款介质波导滤波器的优化设计. 为验证算法性能,将本文算法与三个著名的群智能算法进行比较. 实验结果表明,HHO算法的收敛速度、效率和精度都明显优于目前业内主流应用的自适应差分进化算法、花粉授粉优化算法和灰狼优化算法.     

基于均匀颜色空间色度匹配系数量化精度分析

针对全彩LED显示屏色度匹配系数量化精度存在的问题,提出了一种基于L*a*b*均匀颜色空间对其进行分析的方法.首先,将L*a*b*均匀颜色空间的颜色宽容量,映射于xyY系统,得到不同颜色区域的颜色宽容量;然后,以CIE-XYZ系统为理论依据,对同基色显示的不同颜色的色度进行匹配,分析在三基色混合过程中得到的混合步长与三维颜色宽容范围的函数关系;最后利用此函数和对应于xyY系统中的颜色宽容量对色度匹配过程中混合步长进行分析,经计算,得到各色度匹配过程的混合步长值,从而转化为色度匹配系数量化精度.

Abstract：

For full-color LED,there is a problem of quantization precision of color matching coefficient.In order to solve this problem,an analysis method based on the L*a*b* Uniform Color Space is proposed.First of all,the color tolerance of the Uniform Color Space was mapped to the L* a* b* system,so the color wide-capacity of the different primary color regions could be obtained.Secondly,using the CIE-XYZ system as the theoretical basis,the different colors shown in the same primary color could be matched,and the functional relationship between the mixed-step length during the process of three-color mixing and the three-dimensional color tolerance range could be established.Finally,the mixed color matching step could be analyzed by using this function and the color tolerance of the xyY system.And the mixed matching step length values which could be transformed into the quantization precision of color matching coefficient were calculated.     

System-in-a-package integration of SAW RF Rx filter stacked on a transceiver chip

Demands for mobile phones with smaller form factor and lower cost have driven enhanced integration of electronics components. However, surface acoustic wave (SAW) filters must be fabricated on piezoelectric substrates, and so they are difficult to monolithically integrate on semiconductor chips. This paper reports on a compact wafer-scale packaged SAW filter stacked over a transceiver chip in a quad flat-pack no-lead (QFN) package. An integrated passive device (IPD) provided redistribution and matching between the SAW filter output and the transceiver input. Both extended global system for mobile communications (EGSM) and DCS filters were evaluated. Results demonstrated that conventional packaging techniques could be used to successfully assemble stacked SAW on transceiver modules without damage. SAW compact models based on the coupling of modes model were developed to facilitate system design. Electromagnetic simulations of coupling between SAW filters and inductors integrated on the transceiver suggested that design care is needed to avoid interactions, especially if an IPD is not used as a spacer. With appropriate design, stacked SAW filter on transceiver offers viable module integration.     

Boundary filter optimization for segmentation-based subband coding

This paper presents boundary optimization techniques for the nonexpansive decomposition of arbitrary-length signals with multirate filterbanks. Both biorthogonal and paraunitary filterbanks are considered. The paper shows how matching moments and orthonormality can be imposed as additional conditions during the boundary filter optimization process. It provides direct solutions to the problem of finding good boundary filters for the following cases: (a) biorthogonal boundary filters with exactly matching moments and (b) orthonormal boundary filters with almost matching moments. With the proposed methods, numerical optimization is only needed if orthonormality and exactly matching moments are demanded. The proposed direct solutions are applicable to systems with a large number of subbands and/or very long filter impulse responses. Design examples show that the methods allow the design of boundary filters with good frequency selectivity     

Design of multirate filter banks by ℋ∞optimization

A procedure is developed to design IIR synthesis filters in a multirate filter bank. The filters minimize the l₂-induced norm of the error system between the multirate filter bank and a desired pure time-delay system. This criterion is reduced to one of ℋ_∞ optimization, for which there is ready-made software     

An overview of median and stack filtering

Within the last two decades a small group of researchers has built a useful, nontrivial theory of nonlinear signal processing around the median-related filters known as rank-order filters, order-statistic filters, weighted median filters, and stack filters. This required significant effort to overcome the bias, both in education and research, toward linear theory, which has been dominant since the days of Fourier, Laplace, and Convolute.We trace the development of this theory of nonlinear filtering from its beginnings in the study of noise-removal properties and structural behavior of the median filter to the recently developed theory of optimal stack filtering.The theory of stack filtering provides a point of view which unifies many different filter classes, including morphological filters, so it is discussed in detail. Of particular importance is the way this theory has brought together, in a single analytical framework, both the estimation-based and the structural-based approaches to the design of these filters.Some recent applications of median and stack filters are provided to demonstrate the effectiveness of this approach to nonlinear filtering. They include: the design of an optimal stack filter for image restoration; the use of vector median filters to attenuate impulsive noise in color images and to eliminate cross luminance and cross color in TV images; and the use of median-based filters for image sequence coding, reconstruction, and scan rate conversion in normal TV and HDTV systems.     

Directional processing of color images: theory and experimentalresults

The processing of color image data using directional information is studied. The class of vector directional filters (VDF), which was introduced by the authors in a previous work, is further considered. The analogy of VDF to the spherical median is shown, and their relation to the spatial median is examined. Moreover, their statistical and deterministic properties are studied, which demonstrate their appropriateness in image processing. VDF result in optimal estimates of the image vectors in the directional sense; this is very important in the case of color images, where the vectors' direction signifies the chromaticity of a given color. Issues regarding the practical implementation of VDF are also considered. In addition, efficient filtering schemes based on VDF are proposed, which include adaptive and/or double-window structures. Experimental and comparative results in image filtering show very good performance measures when the error is measured in the L*a*b* space. L*a*b* is known as a space where equal color differences result in equal distances, and therefore, it is very close to the human perception of colors. Moreover, an indication of the chromaticity error is obtained by measuring the error on the Maxwell triangle; the results demonstrate that VDF are very accurate chromaticity estimators.     

Fast adaptive optimization of weighted vector median filters

Weighted vector median (WVM) filters are effective tools for multichannel signal processing. To obtain the desired filtering behavior and characteristic, the WVM filter weights must be determined in an appropriate manner. In this paper, we first analyze previously defined approaches for WVM filter optimization and show their drawbacks related to derivative computation and vector direction information utilization. Based on this analysis, we propose two fast adaptive algorithms for WVM filter design. Proposed Algorithm I computes locally optimal weight changes at each iteration and updates the filter weights accordingly. This algorithm does not involve derivative computation, thus eliminating the instability caused by derivative approximations utilized in previous approaches. Proposed Algorithm II extends the results from established marginal weighted median optimization methods to the vector case by error metric generalization. Both algorithms can be applied to WVM filters using the L/sub p/ norm, while Algorithm I can operate on more general distance metrics. The presented simulation results show that both algorithms are effective, fast, and stable; they perform well under a wide range of circumstances.     

Entropy-based optimization of wavelet spatial filters

A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20 degrees apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean +/- SD) 6.9 +/- 0.3 ms (monopolar recording), to 4.5 +/- 0.2 ms (normal double differential), 3.7 +/- 0.2 (double differential), and 3.0 +/- 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible.     

Robust design of envelope-constrained filters in the presence ofinput uncertainty

The maximum robustness design of envelope-constrained filters with uncertain input (ECUI) is formulated as a minimax optimization problem in which the set of feasible filters is characterized by nonsmooth matrix inequalities. An efficient design algorithm is developed to solve this problem by use of a newly developed transformation technique. The attractive feature of this algorithm is that the ECUI filter thus designed can allow for the greatest uncertainty in the input signal while still achieving the acceptable filter performance