Design and Analysis of High-Throughput Lossless Image Compression Engine Using VLSI-Oriented FELICS Algorithm

In this paper, the VLSI-oriented fast, efficient, lossless image compression system (FELICS) algorithm, which consists of simplified adjusted binary code and Golomb–Rice code with storage-less $k$ parameter selection, is proposed to provide the lossless compression method for high-throughput applications. The simplified adjusted binary code reduces the number of arithmetic operation and improves processing speed. According to theoretical analysis, the storage-less $ k$ parameter selection applies a fixed $ k$ value in Golomb–Rice code to remove data dependency and extra storage for cumulation table. Besides, the color difference preprocessing is also proposed to improve coding efficiency with simple arithmetic operation. Based on VLSI-oriented FELICS algorithm, the proposed hardware architecture features compactly regular data flow, and two-level parallelism with four-stage pipelining is adopted as the framework of the proposed architecture. The chip is fabricated in TSMC 0.13-$ mu$m 1P8M CMOS technology with Artisan cell library. Experiment results reveal that the proposed architecture presents superior performance in parallelism-efficiency and power-efficiency compared with other existing works, which characterize high-speed lossless compression. The maximum throughput can achieve 4.36 Gb/s. Regarding high definition (HD) display applications, our encoding capability can achieve a high-quality specification of full-HD 1080p at 60 Hz with complete red, green, blue color components. Furthermore, with the configuration as the multilevel parallelism, the proposed architecture can be applied to the advanced HD display specifications, which demand huge requirement of throughput.      

SILEX检测与验证

评述了首次实现星问光学连接的欧洲空间局(ESA)的SILEX计划的检测与验证方法,相信对我国星间光通信技术的研究与发展具有一定的促进作用。     

Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences

We present a nonparametric regression method for denoising 3-D image sequences acquired via fluorescence microscopy. The proposed method exploits the redundancy of the 3-D+time information to improve the signal-to-noise ratio of images corrupted by Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to remove the dependence between the mean and variance of intensity values. This preprocessing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm (which requires no motion estimation) is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria.      

State Space Realization of a Three-dimensional Image Set with Application to Noise Reduction of Fluorescent Microscopy Images of Cells

A method is presented to calculate state space realizations of a three-dimensional image set. It is based on interpreting the image set as the impulse response of a 3D separable system. As an application it is shown how this method, combined with approximation steps, including balanced model reduction, can be used to suppress noise in three-dimensional image sets. The approach was motivated by a practical problem in the analysis of three-dimensional fluorescent microscopy image data of fluorescently labelled cells. The method is illustrated by an analysis of simulated data and experimental data. The proposed approach can also be applied to a two-dimensional image in a straightforward way.Received July 9, 2003; Revised April 20, 2003; Accepted June 11, 2004; First online version published in December 2004     

Runlength-Based Processing Methods for Low Bit-depth Images

Many low bit-depth images are compactly expressed in terms of runlength-based representations. This paper discusses methods for processing such images directly in runlength-represented form and how this can improve processing times for image processing operations. The primary focus in the analysis is on image compression operations, but an explanation of the overall scheme for runlength-based processing is also presented. Tradeoffs between processing times and compression ratio for runlength-based image compression algorithms are discussed. Sketches of runlength-based compression algorithms for some widely used image standards are given, and the algorithms are compared with raster-based counterparts. Important design features needed in implementations of runlength-based image processing are discussed. The main application areas of runlength-based image processing are also explained.      

二维图像放大方法的熵分析

对最近邻插值法、线性插值法、邻域像素交换法3种二维图像放大方法的熵进行了分析,给出了放大到4倍和9倍时的计算方法,并对误差进行了分析,仿真结果证明了所给方法的有效性,并发现线性插值法要好一些。     

一种提高结核菌痰涂片显微图像质量方法

通过痰涂片镜检法检测结核抗酸杆菌,是识别和诊断结核病的一种快速、价廉和特定的检测方法.为提高结核病痰涂片显微图像的质量,提出了一种基于多帧不同曝光图像信息融合,扩展被检测显微图像的动态范围,提高检测视野显微图像质量的实现方法.首先在保持CCD曝光时间不变的条件,通过计算机控制圆盘型光学梯度衰减片旋转定位的方法,摄取同一视场多帧不同曝光量的显微图像序列,从而获取场景的高动态范围显微图像信息.然后利用拉普拉斯金字塔算法将显微图像进行分层,采用图像梯度与信息熵相结合对层次图像进行评价,分配相应的合成权重系数,并用拉普拉斯金字塔逆算法,合成一帧高质量的图像.实验表明,所提出方法能有效地表达亮区和暗区的场景信息,增强图像的细节特征,提高痰涂片显微成像的质量,有利于提高显微图像智能检测的精度.     

空时被动合成孔径阵列处理算法研究

本文介绍了两种被动合成孔径阵列处理算法。第一种算法是通过空时数据的互协方差矩阵估计,来精确估计相位校正因子,以提高孔径合成性能;另一种是在前一种算法基础之上,利用对信号子空间的低秩近似来提高低信噪比环境下相位校正因子的估计性能,从而提高被动合成孔径性能的算法。通过对仿真数据和实验数据的分析和比较,说明这两种新算法在较低信噪比下可以得到比传统的ETAM和后来的时域ETAM算法更好的相位估计与方位分辨性能和信号增益。     

A Two-Dimensional Bandwidth Extrapolation Technique for Polarimetric Synthetic Aperture Radar Images

The resolution of a synthetic aperture radar (SAR) image, in range and azimuth, is determined by the transmitted bandwidth and the synthetic aperture length, respectively. Various superresolution techniques for improving resolution have been proposed, and we have proposed an algorithm that we call polarimetric bandwidth extrapolation (PBWE). To apply PBWE to a radar image, one needs to first apply PBWE in the range direction and then in the azimuth direction, or vice versa . In this paper, PBWE is further extended to the 2-D case. This extended case (2D-PBWE) utilizes a 2-D polarimetric linear prediction model and expands the spatial frequency bandwidth in range and azimuth directions simultaneously. The performance of the 2D-PBWE is shown through a simulated radar image and a real polarimetric SAR image     

基于Fourier-Mellin矩的彩色图像检索

为提高图像检索效率,本文提出一种Fourier—Mellin矩的彩色图像检索方法,该方法利用彩色图像的Hilbert变换下相位一致性和梯度幅值的结构特征.用Fourier-Mellin矩描述图像纹理特征,既利用相应一致性不受图像亮度和对比度变化的特点,叉利用Fourier Mellin矩的旋转不变性的特点,对彩色图像有很好的检索效果。为验证该算法的效率,在三个彩色图像库中进行对比实验,实验表明:该算法明显优于文中比较算法。     

SAR图像匹配算法及实现

合成孔径雷达(SAP)图像的匹配处理是SAR图像后处理及应用的重要环节。本文提出了一种基于图像特征的匹配实现方法。针对两幅SAR图像,该方法先进行边缘检测和区域轮廓提取以及区域特征描述,然后基于图像区域不变矩来实现图像的自动匹配。利用真实SAR图像进行了试验,获得很好的试验结果。     

基于小波变换的核磁共振图像去噪方法

核磁共振图像(MRI)在医学诊断上具有重要的意义，但由于受成像方法限制，图像中一些重要的信息常被噪声所淹没，因此对核磁共振图像进行去噪处理是十分必要的。利用基于小波多尺度分解闲值去除MRI图像噪声可以取得较好的效果。在闲值选取上是根据小波分解后每层的特性，对各层分别选取，将此方法与基于Donoho软阈值去噪进行对比，发现此方法可以有效降低噪声，同时比较好地保持图像细节。     

Image Mining for Modeling of Forest Fires From Meteosat Images

Meteosat satellites with the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) sensor onboard provide remote-sensing images nowadays every 15 min. This paper investigates and applies image-mining methods to make an optimal use of images. It develops a simple, time-efficient, and generic model to facilitate pattern discovery and analysis. The focus of this paper is to develop a model for monitoring and analyzing forest fires in space and time. As an illustration, a diurnal cycle of fire in Portugal on July 28, 2004 was analyzed. Kernel convolution characterized the hearth of the fire as an object in space. Objects were extracted and tracked over time automatically. The results thus obtained were used to make a linear model for fire behavior with respect to vegetation and wind characteristics as explanatory variables. This model may be useful for predicting hazards at an almost real-time basis. The research illustrates how image mining improves information extraction from the Meteosat SEVIRI images     

动目标红外图像配准算法研究

论述了基于红外图像背景边缘特征的块匹配配准算法。在图像背景中,利用灰度变化和边缘特征,并根据红外目标的亮度特征避开有目标的区域,搜索合适的图像块;在块匹配时,将图像块进一步细分,改进了相似度测量算法的准确性.实验证明,整个配准算法简单实用,准确有效。     

Wavelet Techniques for Medical Images Performance Analysis and Observations with EZW and Underwater Image Processing

Massive multiple-input multiple output (mMIMO) is considered as one of the most in demand and innovative technologies for the fifth-generation wireless communication systems. This paper attempts to frame a mMIMO system model, intends to improve the spectral efficiency, energy efficiency and performance gain. Here, the system performance achievements are premeditated in a multi-cell downlink mMIMO system under the core considerations such as “imperfect channel estimation, perfect channel estimation and the effect of interference among cells due to pilot sequences contamination”. The performance gains such as “spatial multiplexing gain, array gain and spatial diversity gain” are considered to maximize in this paper. For attaining this multi-objective function, an improved meta-heuristic algorithm called rider optimization algorithm (ROA) known as trial-based ROA adopted, and analyse the performance of the proposed model by comparing over existing models.

     

相干激光雷达距离像背景抑制方法比较和分析

文章在介绍激光雷达两种背景抑制方法的基础上,对两种方法在仿真图像上的抑制效果进行了对比,并对载噪比和阈值系数与均方根误差的关系做出了定量的描述。     

Data-Level Fusion of Multilook Inverse Synthetic Aperture Radar Images

Although techniques for resolution enhancement in single-aspect radar imaging have made rapid progress in recent years, it does not necessarily imply that such enhanced images will improve target identification or recognition. However, when multiple looks of the same target from different aspects are obtained, the available knowledge increases, allowing more useful target information to be extracted. Physics-based image fusion techniques can be developed by processing the raw data collected from multiple inverse synthetic aperture radar sensors, even if these individual images are at different resolutions. We derive an appropriate data fusion rule to generate a composite image containing enhanced target shape characteristics for improved target recognition. The rule maps multiple data sets collected by multiple radars with different system parameters on to the same spatial-frequency space. The composite image can be reconstructed using the inverse 2-D Fourier transform over the separated multiple integration areas. An algorithm called the Matrix Fourier Transform is proposed to realize such a complicated integral. This algorithm can be regarded as an exact interpolation such that there is no information loss caused by data fusion. The rotation centers need to be carefully selected to properly register the multiple images before performing the fusion. A comparison of the image attribute rating curve between the fused image and the spatially averaged images quantifies the improvement in the detected target features. The technique shows considerable improvement over a simple spatial averaging algorithm and thereby enhances target recognition.