Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.

一种结合卷积自编码和补丁惩罚的生成对抗网络单图像去雨方法

针对传统的图像去雨方法存在去雨图像失真、生成伪影等问题,提出一种结合卷积自编码和补丁惩罚的生成对抗网络单图像去雨方法。首先,该方法采用卷积自编码组成生成器网络,使用对称跳跃连接提高生成器网络的训练效率和收敛性能,实现对图像细节信息和二维信号空间信息的重构;其次,引入马尔可夫鉴别器在图像补丁层次上进行惩罚,去除生成图像中的伪影;最后,提出一种新的精细化损失函数参与训练网络模型,进一步增强模型的去雨深度。采用峰值信噪比和结构相似性作为模型的评价标准,实验结果表明,该方法在现实雨图和合成雨图的去雨处理上都有良好的表现,基本还原了图像细节内容,并保证了较高的视觉质量。

一种基于改进SIFT的视频稳像方法

为提高计算效率并保持良好的稳像效果,本文提出一种基于改进SIFT的视频稳像方法。首先对SIFT进行改进,并命名为BO-SIFT(Binarized Octagonal SIFT)。该算法引入了同心八边形环特征描述子,通过降维和二值化对特征向量进行处理,然后使用汉明距离进行特征点匹配,有效缩短了描述和匹配时间。其次将BO-SIFT算法应用于视频稳像,提取视频帧的特征点进行匹配,并计算出帧与帧之间的运动偏移量,以此实现运动估计。最后采用卡尔曼滤波器对估计出的运动偏移量进行平滑处理,并利用仿射变换对视频帧进行逆向补偿,从而得到稳定的图像序列。实验结果表明:相较于原始的SIFT算法,BO-SIFT算法使稳像时间减少了56.404%;相较于现有的较好算法,BO-SIFT算法稳像后的视频具有更高的平均峰值信噪比。此外,本文算法在不同视频上进行稳像效果测试,也具有一定的可靠性和优越性。

基于回溯原子支撑集的广义正交匹配追踪算法研究

为解决广义正交匹配追踪算法(gOMP)在迭代过程中无法删除匹配错误原子问题,提出了一种新的算法:基于回溯原子支撑集的广义正交匹配追踪算法(BgOMP)。该算法在每次迭代选择原子前,首先检验支撑集中是否存在由先前迭代错误选择的原子,并据此更新原子支撑集。这一改进策略允许算法在每次迭代中回溯并纠正先前的错误。通过仿真实验,使用一维信号的成功重构概率和二维信号的峰值信噪比作为评价指标来验证BgOMP算法的性能,实验结果表明:与原始的gOMP算法相比,BgOMP算法显著提高了信号的重构性能。

基于分式二次规划的互模糊函数赋形方法

在开展认知雷达波形设计时,由于发射波形与接收滤波器的非匹配体制,互模糊函数赋形相比传统模糊函数赋形优化自由度更高。该文针对强杂波条件下微弱运动目标检测问题,以最大化信干噪比为优化准则,提出了一种联合发射相位编码序列与接收滤波器设计的互模糊函数赋形方法。在恒模约束下,优化问题被建模为二次分式规划形式;然后通过引入辅助变量,并利用共轭梯度法求解Stiefel流形空间上的最小化问题,非凸优化据此转化为恒模约束二次优化问题;通过交替循环和类幂迭代算法求得最优解。此外考虑到发射波形受硬件限制而难以实现严格恒模,该文构建了一种低峰均比约束二次优化问题模型,并利用最近邻向量法求得最优解。最后,不同参数下的仿真与实测数据实验表明,该文赋形方法相较于传统方法具有较高的信干噪比增益和收敛速度。

基于纳米微晶玻璃的空间悬浮显示

本文介绍了基于纳米微晶玻璃的空间悬浮显示的研究。使用纳米微晶玻璃作为悬浮显示的承载介质,并设计其实验系统,引入峰值信噪比的图像评价指标,测量激光功率对图像质量的影响,实现高清晰度、高分辨率的空间悬浮显示。

基于相机运动轨迹的鲁棒无人机航拍稳像算法

针对高空颠簸环境影响无人机采集延时稳定图像问题,提出了一种适用于悬停拍摄及移动拍摄的航拍视频防抖算法。通过无人机摄像头获取延时摄影视频,提取全局范围内的部分视频帧进行直方图分布比较,进而确定视频是否是在无人机移动状态下拍摄的,并将视频划分为包含与不包含摄像机主动运动两类。对于包含摄像机主动运动的一类,首先,采用FAST角点检测加光流法进行特征点提取配对;其次,利用RANSAC算法剔除误匹配特征点并进行摄像机运动轨迹估计;最后,利用高斯滤波对运动估计参数进行平滑以得到稳定的摄像机运动轨迹。对于不包含摄像机主动运动的一类,首先,对首帧进行网格划分并基于Harris矩阵提取各网格的特征点;其次,在后续帧对这些特征点进行光流追踪;再次,通过反向光流及Harris矩阵计算,增加特征点约束,完成特征点提取及匹配;最后,利用保留的特征点估计后续帧至首帧的稳定变换。利用该算法对视频进行场景分类及画面稳定,实验结果表明:视频分类模块能正确区分两类视频;对比其他两种方法,稳定后的视频图像平均峰值信噪比提升更大;对于不含摄像机主动运动的视频,可实现画面绝对稳定,图像平均峰值信噪比提升超过39%,而其他两种方法仅提升10%~12%。

基于拉曼光谱数据处理和谱峰识别的变压器油绝缘老化研究

针对变压器油的拉曼光谱分析通常受到噪声和荧光背景等的干扰以及谱峰位置难以识别的问题,提出了一种改进的数据处理和谱峰识别算法,用于变压器油老化评估时的拉曼光谱分析。提出一种自适应Savitzky-Golay滤波法,引入自适应窗口大小拉曼光谱数据进行去噪处理。采用改进的多项式拟合算法对去噪后的数据进行去除荧光背景处理,减小荧光背景对拟合结果的影响。通过数据点与期望的拉曼信号的接近程度为每个数据点赋予权重,以实现更准确的去荧光背景处理。利用谱峰识别技术判别变压器油的老化程度,采用大小两种尺度高斯窗口判别法来识别谱峰,并结合局部加权信噪比(local weighted signal-to-noise ratio,LW_SNR)来判断疑似拉曼谱峰的真实性。最后通过实验验证了所提算法在变压器油老化评估中的有效性。

基于前景区域生成对抗网络的视频异常行为检测研究

为提高视频异常行为检测的准确率,本文提出了一种基于前景区域生成对抗网络的改进方法。通过提取实际视频帧的前景和背景掩码,确定生成对抗网络输出视频帧的待检测前景区域。针对待检测前景区域,应用前景区域峰值信噪比准则,计算异常行为检测得分,完成视频异常行为检测。实验结果表明,本文的检测方法在Avenue数据集、UCSD-Ped1数据集、UCSD-Ped2数据集上均能有效提高视频异常行为检测准确率,并能降低检测运行时间。

离轴数字全息的正一级像自动重建方法

针对离轴数字全息中零级像和共轭像模糊重建像的问题,提出了一种基于峰值信噪比的自动提取正一级频谱并重建其图像的方法。理论上推演并分析了该方法的合理性和可行性,获知了频谱振幅的三个冲激函数点对应于正负一级和零级频谱中心。通过频谱图中的振幅极大值点可确定这三个中心点,然后利用圆形滤波窗提取正一级频谱,并以峰值信噪比为客观评价依据来确定最佳滤波窗半径和重建距离。实验结果表明,所提出的方法可以自动重建离焦全息图像的正一级像,与不滤波重建结果相比,其峰值信噪比提高了32.2%,且比全局阈值分步迭代法提高了7.6%。该方法不依赖于其他先验信息,为离轴全息频谱滤波算法提供了一种新的可行方案。

基于注意力的图像去雾算法探索研究

图像去雾的方法可粗略划分为基于图像增强的去雾算法、基于物理模型的去雾算法、基于深度学习的去雾算法3类。将注意力机制引入传统的暗通道先验去雾算法,可克服该算法存在的某些不足。经MATLAB平台进行实验仿真,并利用主观方法(主观感知)与客观方法(峰值信噪比)对比分析仿真结果,证明引入注意力机制的暗通道先验去雾改进算法具有优化传统暗通道先验算法的效果。

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

A Novel Peak-to-Average Power Ratio Reduction Scheme via Tone Reservation in OFDM Systems

In this paper, a novel signal-to-clipping noise ratio and least squares approximation tone reservation scheme(SCR-LSA TR) is proposed to reduce the peak-to-average power ratio for orthogonal frequency division multiplexing systems. During the SCR procedure, only the element with the maximal amplitude is picked for processing, which not only decreases the algorithm complexity, but also helps to overcome the BER deterioration. With the LSA method, the amplitude of the peak-cancelling signals can approximate to that of the original clipping noise as much as possible. Through the combination of the optimization factor in the LSA method, the classic SCR method can achieve better PAPR reduction with faster convergence. Simulation results show that the proposed SCR-LSA TR scheme has less in-band distortion and smaller out-of-band spectral radiation. The BER of the proposed scheme shows a better performance especially under the 16-QAM over the additive white Gaussian noise channel.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

A Robust Denoising Algorithm for Sounds of Musical Instruments Using Wavelet Packet Transform

In this paper, a robust DWPT based adaptive bock algorithm with modified threshold for denoising the sounds of musical instruments shehnai, dafli and flute is proposed. The signal is first segmented into multiple blocks depending upon the minimum mean square criteria in each block, and then thresholding methods are used for each block. All the blocks obtained after denoising the individual block are concatenated to get the final denoised signal. The discrete wavelet packet transform provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle detail of the signal but decision of optimal decomposition level is very important. When the sound signal corrupted with additive white Gaussian noise is passed through this algorithm, the obtained peak signal to noise ratio (PSNR) depends upon the level of decomposition along with shape of the wavelet. Hence, the optimal wavelet and level of decomposition may be different for each signal. The obtained denoised signal with this algorithm is close to the original signal.

地面数字电视单频点同播4套高清节目的实践及视频质量评估

本文提出了一种利用视频质量评估来优化地面数字电视单频点同播高清节目套数的方法。根据我国地面数字电视(DTMB)信道编码、城市无线覆盖的实际情况,以及编码复用前后的视频序列,进行客观视频质量评估计算结果,优化播出设备参数,实现在保证终端接收视频质量的前提下,尽量提高数字电视无线传输覆盖的频带资源的利用效率。

海量多媒体图像压缩中的冗余零系数消除算法设计

为了提高海量多媒体图像压缩的效果,提出一种针对海量多媒体图像压缩中的冗余零系数消除算法。计算图像信息熵,并构建金字塔模型,以提取多媒体图像的信息特征。将图像压缩分为一维离散余弦变换(DCT)压缩与二维DCT压缩,确保图像压缩后结构不被破坏。结合零系数数量变换曲线,确定最佳聚焦位置,建立冗余零系数消除模型,即可完成压缩图像冗余零系数消除。试验结果表明,所提方法可提高海量多媒体图像压缩的峰值信噪比,并且缩小图像所占用的空间。所提方法能够有效提高图像压缩的质量,使图像结构更加完整,故实际应用价值更高。

一种NLF-对数小波阈值的脉冲干扰降噪方法

在脉冲噪声背景下,传统信号降噪方法性能退化,无法去除强脉冲噪声,而且会出现计算复杂度增加的问题.本文提出一种基于非线性滤波(Nonlinear Filtering, NLF)和小波阈值去噪的联合算法,该方法利用非线性滤波算法抑制强脉冲噪声点干扰,通过改进的对数小波阈值函数去噪方法进行全局处理,快速而且高质量的完成对信号降噪.实验结果表明,本文所提算法对脉冲噪声背景下的多种信号类型都具有可行性,能够获得更高的峰值信噪比,具有很好的降噪效果.

基于动态轮廓曲线的运动图像特征识别研究

针对运动图像帧在识别过程中存在误差大,背景、纹理、边缘对运动目标轮廓边缘影响严重等问题,提出基于动态轮廓曲线的运动图像特征识别方法。首先采用不同大小的抑制强度值对图像轮廓的候选边缘进行处理,将有重叠的像素点加入组合中,通过组合结果,提高图像正确分类的概率,得出图像的轮廓边缘。然后对运动图像中相邻两帧间采用尺度变换,对得出新图像的像素值进行小波分解尺度逼近处理,得出收敛补偿,以Newmark迭代算法为基础,对预处理后的像素点进行提取。最后利用修复优化算法对像素点进行修复,通过拓扑梯度最小重构路径耦合修复方法对运动图像损坏区域进行识别,确定运动图像的拓扑梯度以及轮廓修复的关键点,当峰值信噪比为最大时,运动图像的轮廓像素点已修复完成。实验结果表明,采用所提方法对运动图像帧进行跟踪,帧的匹配度较高,跟踪性能良好,而且能够对图像的细节进行修复,修复效果显著。