Focal-plane processing architectures for real-time hyperspectral image processing

Real-time image processing requires high computational and I/O throughputs obtained by use of optoelectronic system solutions. A novel architecture that uses focal-plane optoelectronic-area I/O with a fine-grain, low-memory, single-instruction-multiple-data (SIMD) processor array is presented as an efficient computational solution for real-time hyperspectral image processing. The architecture is evaluated by use of realistic workloads to determine data throughputs, processing demands, and storage requirements. We show that traditional store-and-process system performance is inadequate for this application domain, whereas the focal-plane SIMD architecture is capable of supporting real-time performances with sustained operation throughputs of 500-1500 gigaoperations/s. The focal-plane architecture exploits the direct coupling between sensor and parallel-processor arrays to alleviate data-bandwidth requirements, allowing computation to be performed in a stream-parallel computation model, while data arrive from the sensors.     

Reduced processing element architecture for parallel local image processing

Abstract

The object of this paper is to propose new architecture which can reduce the number of processing elements for parallel local image processing under the premise of real‐time performance. For large‐sized local image processing, this architecture will save much space as it is suitable for being designed into VLSI chip. For example, the traditional parallel architecture will use 9 PEs for a 3×3 convolution, while the Reduced Processing Element Architecture (RPEA) only requires 2 PEs to achieve the real‐time performance.     

Fast geometric transformations for image processing

The algorithms of affine, bilinear, projective, polynomial, and piecewise polynomial transformations of the raster image are suggested. Application of various iterative procedures allows us to reduce considerably the execution time of these transformations in comparison with the programs by the straightforward formulas application.     

Two-dimensional directional wavelets in image processing

The two-dimensional (2-D) continuous wavelet transform (CWT) is characterized by a rotation parameter, in addition to the usual translations and dilations. This enables is to detect edges and directions in images, provided a directional wavelet is used. First we review the general properties of the 2-D CWT and describe several useful representations. We describe various classes of wavelets, including the directional ones. Then we turn to the problem of wavelet calibration, in particular, the evaluation of the scale and angle resolving power of a wavelet. Finally we discuss several applications of directional wavelets. © 1996 John Wiley & Sons, Inc.     

Cellular logic array processing techniques for high-throughput image processing systems

This paper describes certain image processing techniques within the framework ofcellular automata andnormal algorithms for high-throughput data processing. The central idea on which these techniques have been developed is that a digital image can be treated as acellular automaton configuration, and an image processing operation, as anevolution of the automaton due to an updating rule that describes a relational attribute among the pixel values in a specific neighbourhood. Filtering operations on digital images, like that of thinning, edge detection segmentation, erosion and dilation are modelled and realized using cellular automata.     

归一化算法实现畸变不变图像识别研究

在光电混合联合变换相关器图像识别系统中,对与参考图像完全相同的真目标图像和不同的假目标图像能实现完全识别,但对于与参考图像间存在旋转和比例变化的目标图像却很难识别,甚至无法识别。由此,提出了基于图像质心和最长边的归一化图像处理新算法:根据图像像素总值求取图像质心,根据质心和图像边缘轮廓点之间的长度求出图像的最长边和旋转角度,然后根据统一标准将目标图像和参考图像调整至同一长度和旋转方向的完全相同的图像。最后给出了模拟和实验的相关结果,结果表明,该方法能实现存在旋转和比例变化的图像的畸变不变处理,从而提高了系统的识别能力。     

Optoelectronic pipeline architecture for morphological image processing

An optoelectronic architecture for morphological image processing is presented. The architecture uses the pipelining principle, with its stages being implemented by use of optical fiber-based programmable logic arrays. These arrays are characterized by their high fan-in and fan-out factors, which make them suitable for implementing morphological operations with large structuring elements without decomposition. The pipeline has fewer stages and clock skew can be avoided, thus making the use of higher clock speeds and throughputs possible.     

基于图像处理的人民币碎片匹配定位

提出一种人民币碎片图像在基准图中匹配定位的方法.比较了基于灰度互相关法和基于Radon变换法两种不同的方法,并确定用基于灰度互相关法来获得碎片图像相对于基准图的旋转角度.基于灰度互相关法是将基准图在0°-360°间旋转,在每一度上获得碎片图和基准图的最大互相关系数,所有位置中的最大互相关系数所对应的旋转角度即为碎片图相对于基准图的旋转角度.探讨了获得最佳匹配旋转角度的最优搜索方法,在此基础上得到一种有实用价值的碎片图像匹配定位方法,可获得碎片图在基准图上的正确位置.     

Refractive surface flow visualization using image processing

The importance of the wake-free-surface interaction in the detection, classification, and tracking of submerged objects has led to the development of a simple but effective free-surface visualization technique for use in controlled water-tunnel experiments. An experiment was performed to verify the effectiveness and the applicability of this method. Digital images of a spatially varying sinusoidal grid were acquired as seen through the disturbance pattern on the water surface. Image-processing techniques were used to perform phase demodulation of the distorted image. The resulting image details the outline, location, and extent of the surface deformation in a gray-scale format. Optimal digital filter specifications and spatial grid frequencies were determined experimentally for various surface-flow conditions.     

Channel reduction and applications to image processing

Oftentimes when one is dealing with digital color images it is desired that some sort of image processing be performed on the spatial information. Current methods require that one process each of the channels (also called planes or colors) of an image separately, which increases the number of computations significantly. A novel, to our knowledge, approach to reducing the number of channels in a color image is presented. The channel-reduction process is intended to facilitate such color image-processing situations essentially by the separation of the spectral information from the spatial information, as in a paint-by-numbers picture. In this case the image processing need be applied only to a single channel of data and the color added afterward. With a compression ratio of slightly less than 3:1 the method is not intended to compete with existing compression methods but rather to permit the processing of images in a compressed state.     

Signal processing techniques for CCD image sensors

Charge coupled device (CCD) image sensors are currently used in many image processing applications such as facsimile and bar code reading. Because the photodetectors in the device have finite areas, the output signal from the CCD image sensor is only an approximation to the sampled value of the image. In this paper we discuss the reconstruction of bar codes from the imperfect CCD signal by digital signal processing techniques. Schemes for encoding digital data in the form of bar codes are also discussed.     

数字图像处理在距离测量中的应用

为了克服接触式测量中的一些弊端,如测量时加在物体上的物理效应、测量环境的高要求、人为误差较大等,结合了现代数字图像处理技术和视觉测量方法,采用了非接触式测量方法进行距离测量.建立了图像采集系统,运用Labwindows/CVI软件设计的Sobel算子对采集到的图像进行边缘检测,得到两边缘线之间的像素差值,并代入距离计算公式求得距离.结果表明,该测量方法所得结果与实际值符合,证实了基于数字图像处理的距离测量方法的准确性.     

Filter for biomedical imaging and image processing

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a priori, knowledge about the type of noise corrupting the image. This makes the standard filters application specific. Widely used filters such as average, Gaussian, and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high-frequency details, making the image nonsmooth. An integrated general approach to design a finite impulse response filter based on Hebbian learning is proposed for optimal image filtering. This algorithm exploits the interpixel correlation by updating the filter coefficients using Hebbian learning. The algorithm is made iterative for achieving efficient learning from the neighborhood pixels. This algorithm performs optimal smoothing of the noisy image by preserving high-frequency as well as low-frequency features. Evaluation results show that the proposed finite impulse response filter is robust under various noise distributions such as Gaussian noise, salt-and-pepper noise, and speckle noise. Furthermore, the proposed approach does not require any a priori knowledge about the type of noise. The number of unknown parameters is few, and most of these parameters are adaptively obtained from the processed image. The proposed filter is successfully applied for image reconstruction in a positron emission tomography imaging modality. The images reconstructed by the proposed algorithm are found to be superior in quality compared with those reconstructed by existing PET image reconstruction methodologies.     

Real-time image deblurring by optoelectronic hybrid processing

An efficient approach is presented to restore a motion-blurred image in real time by optoelectronic hybrid processing, by which an image motion vector can be effectively detected and an accurate point spread function is constructed rapidly. A simple Wiener filter algorithm is employed to restore the blurred image. It greatly alleviates the complexity of the restoration algorithm. The proposed approach can apply to arbitrary motion-blurred image restoration. An optoelectronic hybrid joint transform correlation is constructed to verify the efficiency. The experimental results show that the proposed method has distinct advantages of preferable effect and good real time.     

Differential operator approach for Fourier image processing

We present a differential operator approach for Fourier image processing. We demonstrate that when the mask in the processor Fourier plane is an analytical function, it can be described by means of a differential operator that acts directly on the input field to give the processed output image. In many cases (e.g., Schlieren imaging) this approach simplifies the calculations, which usually involve the evaluation of convolution integrals, and gives a new insight into the image-processing procedure.     

Adaptive methods and system for image processing

We present results obtained in the implementation of adaptive methods and a system for image processing. The light-developing nonlinear elements are used in the filtering block of the mirror optical configuration. The processing of gray-scale and of binary images with the help of adaptive optical elements is fulfilled for realization of ground-suppressing, contouring, differentiating, and skeletonizing adaptive filters. A comparison is made between optical and digital methods applied in the pattern processing.     

Fuzzy singular value shrinkage for Poisson image denoising

Poisson noise is a fundamental problem in various imaging applications, such as low-light photography, computed tomography and fluorescence microscopy. To remove Poisson noise, an adaptive iterative singular value shrinkage algorithm based on variance-stabilizing transformation (VST) and fuzzy logic classification is proposed in this paper. Since Poisson noise is signal dependent, we use the VST to convert it into signal independent Gaussian noise. The transformed image is divided into a number of blocks, and the similarity of these blocks is well judged according to the similarity criterion of an approximate Kullback–Leibler (KL) distance, and they are arranged to form a low-rank matrix. Then, the proposed algorithm uses singular value decomposition and adaptive soft-thresholding contraction operator to reduce noise, because large singular values point to the position of interesting information, and small singular values point to the position of the noise. In addition, to better preserve the structural information of the image, an adaptive iterative regularization technique based on fuzzy logic classification is proposed. Finally, a potentially noise-free image is obtained by unbiased inverse VST. Experimental results show that the proposed algorithm is competitive with several popular Poisson denoising techniques in both visual and objective metrics.