Development of afterglow time test system for nanosecond fluorescent screen of low-level-light image intensifier北大核心CSCD

The time characteristics of fluorescent screen is one of the important parameters to evaluate the performance of image intensifier. At present, there is no measurement method for the afterglow time of nanosecond fluorescent screen of low-level-light image intensifier. Based on the traditional test scheme of image intensifier afterglow time, a afterglow time test system for nanosecond fluorescent screen was developed. This system used a high-speed signal generator with the sampling rate of 250 MHz to complete the excitation of the laser diode light pulse, and a photomultiplier tube was used with the descending time of 0.57 ns to complete the photoelectric conversion of the fluorescent screen light signal. The weak photocurrent signal of μA magnitude was amplified and converted to a single-terminal differential circuit to complete the AD conversion in AD9684. Then the digital luminance information of the fluorescent screen was stored in the double data rate SDRAM (DDR) unit after field programmable gate array (FPGA), and the host computer sent instructions to read the DDR memory. The USB3.0 high-speed transmission protocol was used to transmit data to the host computer. In the data processing, the Kalman filtering and fast finding falling edge algorithm were used to realize the accurate measurement of noise filtering from collected data and afterglow time. The test results show that the proposed afterglow time test system for nanosecond fluorescent screen can effectively test the image intensifier with ultrafast optical characteristics. The afterglow test results of P47 phosphor reaches 118.094 4 ns, and the repeatability reaches 2.08%. © 2022 Editorial office of Journal of Applied Optics. All rights reserved.     

Adaptively wavelet-based image denoising algorithm with edge preserving

A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge information. This method is adaptive to local image details, and can achieve better performance than the methods of state of the art.     

Adaptively wavelet-based image denoising algorithm with edge preserving

A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband.Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge information. This method is adaptive to local image details, and can achieve better performance than the methods of state of the art.     

Virtual reproduction of 5.1 channelsurround sound by headphone

An improved signal processing algorithm for virtual reproducing 5.1 channel surround sound by headphone is proposed. Firstly, by analyzing current signal processing algorithm, it is pointed out that current algorithms will create unnatural subjective hearing effect. Then, by using the method of HRTF (Head-related transfer function) filtering and surround signal decorrelating, the improved algorithm is able to eliminate the defect of in-head-localization in headphone reproduction and simulate the subjective effect of multi surround loudspeakers while not to induce other unnatural effect. Therefore, the improved signal processing algorithm can increase envelopment and improve the subjective effect in headphone reproduction.     

Speckle suppressing based on morphological filter and fuzzy logic

A novel morphological filtering algorithm is proposed for suppressing speckle noise in images. The algorithm employs directional morphological close-open and open-close operations, then computing the membership of the filtered versions' every pixel according to the designed fuzzy rule. The final filtered image is composed of all the pixels with corresponding maximal membership. The validity of the algorithm is demonstrated.     

Robust color segmentation algorithms in illumination variation conditions

<正>Changing illumination condition can change the result of image segmentation algorithm and reduce the intelligent recognition rate.A novel color image segmentation method robust to illumination variations is presented.The method is applied to the skin segmentation.Based on the hue preserving algorithm, the method reduces the dimensionality of the red-green-blue(RGB) space to one dimension,while keeping the hue of every pixel unchanging before and after space transformation.In the new color space,the skin color model is established using Gaussian model.Experimental results show that the method is robust to illumination variations,and has low computational complexity.     

Rotation-invariant texture analysis using Radon and Fourier transforms

Texture analysis is a basic issue in image processing and computer vision,and how to attain the rotation- invariant texture characterization is a key problem.This paper proposes a rotation-invariant texture analysis technique using Radon and Fourier transforms.This method uses Radon transform to convert rotation to translation,then utilizes Fourier transform and takes the moduli of the Fourier transform of these functions to make the translation invariant.A k-nearest-neighbor rule is employed to classify texture images.The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step.Experiment results show the feasibility of the proposed method and its robustness to additive white noise.     

Rotation-invariant texture analysis using Radon and Fourier transforms

Texture analysis is a basic issue in image processing and computer vision, and how to attain the rotationinvariant texture characterization is a key problem. This paper proposes a rotation-invariant texture analysis technique using Radon and Fourier transforms. This method uses Radon transform to convert rotation to translation, then utilizes Fourier transform and takes the moduli of the Fourier transform of these functions to make the translation invariant. A k-nearest-neighbor rule is employed to classify texture images. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. Experiment results show the feasibility of the proposed method and its robustness to additive white noise.     

Virtual reproducing system for 5.1 channelsurround sound

An improved virtual reproducing system for 5.1 channel surround sound and signal processing algorithm of the system are proposed. Firstly, by analyzing the sound field of virtual reproducing systems for 5.1 ch surround sound, it is proved that there are some defects in current systems, including narrow listening area, sound image direction distortion, timbre change in reproduction etc. Then from theoretical analysis, it is proposed that, by decreasing the separated angle between reproducing loudspeaker pair to ±15° and using signal processing algorithm with timbre equalization, the improved system can get rid of the defects of current systems. Additionally, the signal processing algorithm of improved system is also simple. The results of psychoacoustic experiment verify theoretical analysis, and show that improved system is able to recreate sound image in half of horizontal plane (about±90°) in the front. Finally, the system and its arrangement of loudspeakers are suitable to be used in TV sets or mu     

New enhancement of infrared image based on human visual system

Infrared images are firstly analyzed using the multifractal theory so that the singularity of each pixel can be extracted from the images. The multifractal spectrum is then estimated, which can reflect overall characteristic of an infrared image. Thus the edge and texture of an infrared image can be accurately extracted based on the singularity of each pixel and the multifractal spectrum. Finally the edge pixels are classified and enhanced in accordance with the sensitivity of human visual system to the edge profile of an infrared image. The experimental results obtained by this approach are compared with those obtained by other methods. It is found that the proposed approach can be used to highlight the edge area of an infrared image to make an infrared image more suitable for observation by human eyes.     

一种新的激光雷达距离像噪声抑制方法

主要针对激光雷达距离像的距离反常噪声抑制问题,阐述了激光雷达距离像的噪声原理,分析了应用传统中值滤波方法抑制距离反常噪声的缺陷,提出了基于包围准则的自适应中值滤波算法。该方法首先根据包围准则检测噪声,对5×5滤波窗口内的像素值进行排序差分;然后选择低于门限长度最长的连续差分值对应的像素值作为距离正常值;最后运用中值滤波和加权均值滤波进行噪声抑制。实验结果表明,该方法有效抑制了距离反常噪声,且较好地保护了距离图像中目标的边缘细节,均方根误差分别比3×3和5×5窗口中值滤波法减少了27.1%和9.1%。     

基于小波变换的图像混合噪声自适应滤除算法

为同时滤除图像中的椒盐噪声和高斯噪声,提出了一种基于小波变换的混合噪声自适应滤除算法,该算法首先采用中值滤波去除椒盐噪声,然后借助边缘检测算子区将图像为分边缘与非边缘区域,进一步对非边缘区域引入改进的均值滤波器,有效削弱高斯噪声的同时保护图像边缘细节,既初步削弱高斯噪声又保护了边缘,最后采用改进的小波阈值滤波算法,对不同的小波系数采用不同的阈值函数,通过线性回归得到各最优阈值关系式。实验结果表明,该混合噪声自适应滤除算法能有效滤除椒盐噪声和高斯噪声,在图像主观质量和客观质量上均取得了较好的效果,能提高去噪图像峰值信噪比0.5~2.0 dB。     

基于各向异性扩散的红外图像噪声滤波算法

抑制红外焦平面阵列探测器噪声时,为了有效保持边缘细节,在塔基(Tukey)扩散模型的基础上,提出了一种基于各向异性扩散的滤波算法.算法由微分法提取边界点集,利用信号和噪声的空间分布特性对图像进行预处理,从而改善了Perona-Malik(P-M)方程的病态性.建立了新的扩散系数且该系数在理论上满足Charbonnier等人构造准则.通过给定边界条件实现了有利于保持边缘的目的.该算法已在基于DSP FPGA的硬件平台上实时运行,较好地抑制了噪声,又有效地保持了边缘,处理后的图像具有良好的视觉效果.     

一种照明无关的小波多尺度相乘边缘检测方法

提出了一种照明无关的小波多尺度相乘边缘检测方法,用于从非均匀的弱照明图像中提取边缘。根据照明反射图像形成模板与CCD相机成像原理,推导出图像的对应小波变换公式。然后,对图像局部区域中噪声、边缘与背景像素的小波系数进行比较分析,设计了一种照明无关的小波边缘检测公式。为增强边缘并抑制噪声,提出了一种改善的小波多尺度相乘边缘检测方法,并依照小波变换后边缘像素的特征,提取单像素的边缘。采用仿真和真实的非均匀的弱照明图像对该边缘检测算法进行验证,并与另外两种边缘检测方法进行定性的和定量的比较。实验结果证实了这种边缘检测方法能够从灰度不均匀的低衬比度图像中正确有效地提取边缘。     

闪光照相CCD图像的自适应中值滤波方法

中值滤波是一种在去除噪声的同时能较好地保护图像边缘细节的非线性图像处理方法。为了滤除闪光照相CCD图像中的脉冲噪声，同时能更好地保护图像边缘，提出了一种改进的自适应中值滤波方法。该方法采用局部中值和局部方差作为判断噪声点的阈值，实现了局部自适应的中值滤波，克服了传统自适应中值滤波方法的缺点，对椒盐噪声和随机脉冲噪声均有较好的滤波效果。实验结果表明，该方法消除图像脉冲噪声十分有效，对闪光照相CCD图像的处理结果也较好。     

基于改进的Canny算子实时视频边缘检测系统在FPGA上的设计与实现

为了提高图像边缘检测的性能,缩短处理时间,提出了一种基于FPGA的实时视频边缘检测系统。该系统以EP2C8Q208C8为实验硬件平台,首先采用摄像头OV7670获取模拟视频数据,双端口SDRAM实现对图像数据的缓存,利用FPGA并行处理的特点,采用Verilog HDL硬件描述语言实现改进的Canny边缘检测算法,最终实现在VGA显示屏上显示图像边缘的效果。实验结果表明,较传统的边缘检测算法,该系统边缘检测定位精度高,对噪声的抗干扰能力强,能够准确快速的输出图像边缘信息。     

模糊权值中值滤波的X射线图像消噪算法

针对X射线荧光图像的低亮度及噪音造成的对比度差和图像模糊的问题,提出了一种模糊权值中值滤波的图像消噪算法.先用模糊邻域检测法结合梯度检测法分离原始图像中的噪音点与非噪音点,然后在噪音点的邻域内,计算各像素点与邻域中值的模糊相似度,用相似度作中值的权值,对噪音点邻域进行加权滤波|这种算法使图像的非噪音点保持不变.实验结果表明, 本文算法不仅具有较强的消噪能力,而且能够保持图像的边缘细节和纹理信息.     

亚像元边缘检测系统的FPGA实现

提出了一种集成于单片FPGA的数字化线阵CCD边缘检测系统。通过对CCD输出图像的边缘灰度梯度分析,利用高斯滤波除噪、边缘检测算法确定其像元级边界,并提出了一种以最小二乘多项式拟合来确定亚像元级边界位置的新算法。整个系统以现场可编程门阵列器件作为核心及数字电路硬件的载体,利用VHDL语言及图形化输入方式在QuartusⅡ7.2软件平台上进行了系统的设计。误差分析及仿真结果表明,该边缘检测系统的分辨率可达到将近六十分之一的像元宽度,可应用于研制高精度CCD光电自准直仪。     

基于FPGA的准直测角系统

为了提高线阵CCD准直测角系统的精度、速度、集成度,以及可靠性,提出一种集成于单片FPGA、光积分时间可自适应调节的片上准直测角系统。以FPGA作为系统处理核心,利用高斯滤波算法对CCD输出信号进行数字滤波,利用边缘自动检测算法获得图像的像元级边界,在此基础上应用多项式插值算法对图像边缘位置进行亚像元细分,最终计算出失准角。利用VHDL语言以及MAX+plus II软件完成了系统设计。系统仿真和实验表明,所设计的片上系统具有高精度、高集成度、高可靠性的特点。     

Switching non-local vector median filter

This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images.