Fast maximum intensity projections of large medical data sets by exploiting hierarchical memory architectures.

Maximum intensity projections (MIPs) are an important visualization technique for angiographic data sets. Efficient data inspection requires frame rates of at least five frames per second at preserved image quality. Despite the advances in computer technology, this task remains a challenge. On the one hand, the sizes of computed tomography and magnetic resonance images are increasing rapidly. On the other hand, rendering algorithms do not automatically benefit from the advances in processor technology, especially for large data sets. This is due to the faster evolving processing power and the slower evolving memory access speed, which is bridged by hierarchical cache memory architectures. In this paper, we investigate memory access optimization methods and use them for generating MIPs on general-purpose central processing units (CPUs) and graphics processing units (GPUs), respectively. These methods can work on any level of the memory hierarchy, and we show that properly combined methods can optimize memory access on multiple levels of the hierarchy at the same time. We present performance measurements to compare different algorithm variants and illustrate the influence of the respective techniques. On current hardware, the efficient handling of the memory hierarchy for CPUs improves the rendering performance by a factor of 3 to 4. On GPUs, we observed that the effect is even larger, especially for large data sets. The methods can easily be adjusted to different hardware specifics, although their impact can vary considerably. They can also be used for other rendering techniques than MIPs, and their use for more general image processing task could be investigated in the future.     

Adaptive fuzzy systems for multichannel signal processing

Processing multichannel signals using digital signal processing techniques has received increased attention lately due to its importance in applications such as multimedia technologies and telecommunications. The objective of this paper is twofold: 1) to introduce adaptive filtering techniques to the reader who is just beginning in this area and 2) to provide a review for the reader who may be well versed in signal processing. The perspective of the topic offered here is one that comes primarily from work done in the field of multichannel (color) image processing. Hence, many of the techniques and works cited here relate to image processing with the emphasis placed primarily on filtering algorithms based on fuzzy concepts, multidimensional scaling, and order statistics-based designs. It should be noted, however, that multichannel signal processing is a very broad field and thus contains many other approaches that have been developed from different perspectives, such as transform domain filtering, classical least-square approaches, neural networks, and stochastic methods, just to name a few. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. The strong potential of fuzzy adaptive filters for multichannel signal applications, such as color image processing, is illustrated with several examples     

On filtering by means of generalized integral images: a review and applications

In this paper the method of multidimensional (n-D) filtering based on prior signal integration is analyzed. This method has the advantage that the computational complexity for filtering is independent of the filter kernel size. An overview of recent 2-D image processing systems is presented where these types of filters are applied. Based on this overview a framework that covers this class of filters is derived using repeated integration. These filters include for example rect and triangle-filters which can be used to approximate Gaussian derivative filters. Furthermore the normalization of the filters, computational complexity, and storage cost are discussed. Finally, two image processing systems which benefit from the application of the filters are presented. They belong to the topic of advanced driver assistance systems.     

Local Interpolation-based Polar Format SAR: Algorithm, Hardware Implementation and Design Automation

In this paper we present a local interpolation-based variant of the well-known polar format algorithm used for synthetic aperture radar (SAR) image formation. We develop the algorithm to match the capabilities of the application-specific logic-in-memory processing paradigm, which off-loads lightweight computation directly into the SRAM and DRAM. Our proposed algorithm performs filtering, an image perspective transformation, and a local 2D interpolation, and supports partial and low-resolution reconstruction. We implement our customized SAR grid interpolation logic-in-memory hardware in advanced 14 nm silicon technology. Our high-level design tools allow to instantiate various optimized design choices to fit image processing and hardware needs of application designers. Our simulation results show that the logic-in-memory approach has the potential to enable substantial improvements in energy efficiency without sacrificing image quality.     

A new approach to vector median filtering based on space fillingcurves

The availability of a wide set of multidimensional information sources in different application fields (e.g., color cameras, multispectral remote sensing imagery devices, etc.) is the basis for the interest of image processing research on extensions of scalar nonlinear filtering approaches to multidimensional data filtering. A new approach to multidimensional median filtering is presented. The method is structured into two steps. Absolute sorting of the vectorial space based on Peano space filling curves is proposed as a preliminary step in order to map vectorial data onto an appropriate one-dimensional (1-D) space. Then, a scalar median filtering operation is applied. The main advantage of the proposed approach is the computational efficiency of the absolute sorting step, which makes the method globally faster than existing median filtering techniques. This is particularly important when dealing with a large amount of data (e.g., image sequences). Presented results also show that the filtering performances of the proposed approach are comparable with those of vector median filters presented in the literature.     

A defect-tolerant systolic array implementation for real time image processing

An advanced defect tolerant systolic array implementation of the 2D convolution algorithm for real time image processing applications has been full-custom designed and fabricated using standard CMOS technology. The bit-serial systolic array incorporates new architectural concepts and circuit techniques fitting a defect tolerant design approach. Therefore high performance and high yield enhancement is achieved.The defect tolerance techniques are based on software controlled defect localization and reconfiguration with programmable switches by a host-processor or a VLSI-tester.The chips functionality differs to available convolution chips by the maximum kernel size of 256 taps, the ability to convolve one video signal with up to four independent coefficient masks, support of adaptive filtering, on-chip line delays and implemented special processing of frames borders.High performance implementations of signal processing algorithms require large chip die sizes. The presented defect tolerance techniques and architectural concepts make systolic large area implementations of signal processing algorithms feasible.     

结合张量空间与倒易晶胞的高光谱影像去噪去混叠

传统去噪去混叠算法大多针对单波段图像,针对于高光谱影像的特点以及噪声、混叠对于图像的影响,提出了一种结合张量与倒易晶胞的多维滤波算法,并将其应用在高光谱影像的去噪和去混叠中。该方法引入张量,将高光谱影像数据视为三阶的张量表达,以倒易晶胞获取影像混叠和噪声较小的频谱覆盖,从最小均方误差的角度交替迭代求解三个方向的滤波器,最终完成影像滤波,在保证影像空间和光谱信息一致性的前提下,有效地减少影像混叠和噪声,提高图像的质量。通过与二维维纳滤波算法、张量多维去噪算法的多组高光谱数据对比实验,证明了文中算法的有效性。     

Realizations of fast 2-D/3-D image filtering and enhancement

This paper proposes a novel algorithm for multidimensional image enhancement based on a fuzzy domain enhancement method, and an implementation of a recursive and separable low-pass filter. Considering a smoothed image as a fuzzy data set, each pixel in an image is processed independently, using fuzzy domain transformation and enhancement of both the dynamic range and the local gray level variations. The algorithm has the advantages of being fast and adaptive, so it can be used in real-time image processing applications and for multidimensional data with low computational cost. It also has the ability to reduce noise and unwanted background that may affect the visualization quality of two-dimensional (2-D)/three-dimensional (3-D) data. Examples for the applications of the algorithm are given for mammograms, ultrasound 3-D images, and photographic images.     

Enhanced detectability of small objects in correlated clutter usingan improved 2-D adaptive lattice algorithm

Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.     

Modeling and Evaluating Non-shared Memory CELL/BE Type Multi-core Architectures for Local Image and Video Processing

Local processing, which is a dominant type of processing in image and video applications, requires a huge computational power to be performed in real-time. However, processing locality, in space and/or in time, allows to exploit data parallelism and data reusing. Although it is possible to exploit these properties to achieve high performance image and video processing in multi-core processors, it is necessary to develop suitable models and parallel algorithms, in particular for non-shared memory architectures. This paper proposes an efficient and simple model for local image and video processing on non-shared memory multi-core architectures. This model adopts a single program multiple data approach, where data is distributed, processed and reused in an optimal way, regarding the data size, the number of cores and the local memory capacity. The model was experimentally evaluated by developing video local processing algorithms and programming the Cell Broadband Engine multi-core processor, namely for advanced video motion estimation and in-loop deblocking filtering. Furthermore, based on these experiences it is also addressed the main challenges of vectorization, and the reduction of branch mispredictions and computational load imbalances. The limits and advantages of the regular and adaptive algorithms are also discussed. Experimental results show the adequacy of the proposed model to perform local video processing, and that real-time is achieved even to process the most demanding parts of advanced video coding. Full-pixel motion estimation is performed over high resolution video (720×576 pixels) at a rate of 30 frames per second, by considering large search areas and five reference frames.     

结合频域显著性分析和形态学滤波的红外小目标检测算法北大核心CSCD

针对复杂背景下的红外小目标检测问题,提出了基于频域显著性分析和形态学滤波相结合的图像处理算法。通过构建图像频域显著性分析图,并引入自适应阈值实现显著图的分割,提取感兴趣区域(ROI)以确定候选目标,达到凸显目标并抑制背景的目的。为抑制残余的杂波干扰,基于候选目标轮廓尺寸自适应并引入新的评价机制实现滤波结构元的筛选,采用新型Top-hat滤波技术消除伪目标响应。本算法充分利用红外小目标的频域显著性和空域尺寸先验信息的互补性实现检测,在设计中则平衡了计算复杂度与处理效果的矛盾关系,在有效提升检测指标的同时保证了实时计算的工程需求。实验表明本文提出的算法能显著提高目标信噪比,高效和准确地检测噪声背景下的弱小红外目标。     

Two-dimensional signal processing techniques for airborne laserbathymetry

The application of two-dimensional (2D) signal processing to data collected in airborne laser bathymetry is investigated. Specifically, a type of 2D filter for the suppression of impulsive noise in irregularly-spaced data based on order-statistics filtering is developed. An algorithm which incorporates this type of filter along with a sophisticated 2D interpolation technique is constructed to automate the filtering process. An adaptive 2D filtering technique that can be applied to raw bathymetric profiles to remove wideband noise is then discussed. The results obtained show that each type of filtering enhances the accuracy of bathymetric measurement quite significantly     

High-performance template tracking

Tracking systems are important in computer vision, with applications in video surveillance, human computer interfaces (HCI), etc. Consumer graphics processing units (GPUs) have experienced an extraordinary evolution in both computing performance and programmability, leading to a greater use of the GPU for non-rendering applications, such as image processing and computer vision tasks. In this work we show an effective particle filtering implementation for real-time template tracking based on the use of a graphics card as a streaming architecture in a translation-rotation-scale model.     

Sidelobe reduction via adaptive FIR filtering in SAR imagery.

The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.     

Reduction of noise in AVHRR channel 3 data with minimum distortion

The channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of weather satellites (NOAA 6-12) are contaminated by instrumentation noise. The signal to noise ratio (S/N) varies considerably from image to image and the between sensor variation in S/N can be large. The characteristics of the channel noise in the image data are examined using Fourier techniques. A Wiener filtering technique is developed to reduce the noise in the channel 3 image data. The noise and signal power spectra for the Wiener filter are estimated from the channel 3 and channel 4 AVHRR data in a manner which makes the filter adaptive to observed variations in the noise power spectra. Thus, the degree of filtering is dependent upon the level of noise in the original data and the filter is adaptive to variations in noise characteristics. Use of the filtered data to improve image segmentation, labeling in cloud screening algorithms for AVHRR data, and multichannel sea surface temperature (MCSST) estimates is demonstrated. Examples also show that the method can be used with success in land applications. The Wiener filtering model is compared with alternate filtering methods and is shown to be superior in all applications tested     

Multidimensional Fuzzy Filtering using Statistical Parameters

A multidimensional filtering technique is proposed using fuzzy logic ideas and based on two locally estimated statistical parameters. This technique is applied to the 3D space of color images. Two fuzzy sets for each fuzzy variable are considered. These fuzzy variables assign the corresponding part of the image to a specific class. The parameters of the fuzzy sets are derived in respect to the corresponding crisp sets attributed to the ideal noiseless image. An additional fuzzy selection process is also introduced by applying a distance dependent weighted average as the filtering action of each rule. Both signal and noise characteristics are taken into account in the filtering process. Experimental results show that these fuzzy non-linear filters work very well.     

Multidimensional modeling of image quality

In this paper, multidimensional models of image quality are discussed. In such models, alternative images, for instance, obtained through different processing or coding of the same scene, are represented as points in a multidimensional space. The positioning is such that the correlation between geometrical properties of the points and the subjective impressions mediated by the corresponding images is optimized. More specifically, perceived dissimilarities between images are monotonically related to interpoint distances, while the strengths of image quality attributes (such as perceived noise and blur or image quality) are, for instance, monotonically related to point coordinates along specified directions. The goal of multidimensional models is to capture subjective impressions into a single picture that is easy to interpret. We apply multidimensional models to two existing data sets to demonstrate that they indeed account very well for experimental data on image quality. The program XGms is introduced as a new interactive tool for constructing multidimensional models from experimental data. Although XGms is introduced here within the context of image-quality modeling, it is also potentially useful in other applications that rely on multidimensional models     

基于数字信号处理器的激光成像雷达目标识别算法实现

激光成像雷达的空间分辨率较高,能成四维像(强度像 三维距离像),适合作目标识别探测器.支持向量机(SVM)是一种能在小样本学习的情况下,仍有较高识别正确率的目标识别方法.通过优化支持向量机算法,将它嵌入到激光成像雷达系统的数字信号处理器(DSP)芯片内,实现目标识别的功能,有很高的现实意义.首先用真实激光成像雷达强度像做实验,测试56个样本,共耗时31.97μs,证明嵌入到数字信号处理器的支持向量机算法能满足实时性要求,识别正确率为98.2%;再用仿真激光成像雷达距离像验证支持向量机的推广能力,证明支持向量机在实时性和识别性能两方面都能满足激光成像雷达的识别要求.     

A novel smart antenna system implementation for broad-band wirelesscommunications

A novel design of smart antenna system with adaptive beamforming capability is introduced for broad-band wireless communication. To achieve high data throughput of the multi-antenna system, a parallel analog-digital (A/D) signal processing scheme is proposed. The essential idea is to realize the real-time beamforming using heterodyne RF and IF circuitry. The bottleneck of digital signal processor (DSP) I/O and processing speed is thus relieved, while the advanced signal processing capability of the DSP chip is utilized. Based on this idea, a 5.8 GHz smart antenna receiver is implemented. Various experiments are carried out to examine the direction of arrival (DOA) estimation, beam synthesis, and bit error rate (BER) performances of the system. A 20-Mb/s data throughput using binary phase shift keying (BPSK) modulation is demonstrated for this eight-element adaptive array     

Adaptive maximum SINR RAKE filtering for DS-CDMA multipath fadingchannels

The conventional signature-matched RAKE processor for multipath direct-sequence code division multiple access channels is viewed as a regular linear tap-weight filter of length equal to the sum of the system processing gain and the user channel memory. In this paper, performance improvements are sought in the context of adaptive filtering under maximum signal-to-interference-plus-noise-ratio criteria. The minimum-variance-distortionless-response RAKE (RAKE-MVDR) filter and the lower complexity scalar optimized auxiliary-vector RAKE (RAKE-AUX) filter are developed. Bit error rate (BER) comparisons with the conventional RAKE signature-matched filter are carried out for training sets of reasonably small size, perfectly known, and mismatched/estimated channel coefficients, and extreme near-far system configurations