基于Surfacelet变换视频自适应去噪

为了有效地保留视频去噪后的纹理细节,利用Surfacelet变换特性和自适应阈值的方法,提出了一种新的视频去噪算法。Surfacelet变换是超小波分析领域的最新发展,具有良好的多维信号处理能力,真正实现了对多维信号的多方向多尺度分解。本文介绍了Surfacelet变换的结构,根据Surfacelet变换的视频信号的系数特性和噪声分布特征,自适应估计每个方向子带的系数阈值。实验表明,本文算法能更好保留视频细节信息,对于视频的噪声也具有更强的抑制能力。     

基于时域的视频去噪算法研究

论述了两种基于时域的视频去噪算法。首先介绍基于菱形运动估计的时域滤波,先进行菱形运动估计计算寻找最佳匹配块,之后在两个块之间进行均值滤波,整合匹配块输出滤波过的图像;其次介绍了BM3D视频去噪技术,详细地描述了BM3D去噪技术的去噪主要过程,分别解释了去噪过程中块匹配、三维变换域滤波、权值计算、重构四个主要步骤的原理与过程。最后通过实验,证明了这两种方法在去噪方面均能取得良好的效果。     

红外偏振感知与智能处理

红外偏振成像在抗干扰目标检测、复杂环境下人造物识别中具有潜在优势,同时能够获取目标表面理化特性。分时、分振幅、分孔径红外偏振成像方式由于体积、重量、功耗等的不足限制了其应用,而小型化、集成化、实时成像设备是红外偏振成像广泛应用的前提,而对于所获取数据的智能分析是其应用的基础。介绍了所研制的红外偏振智能感知系统,通过分焦平面式成像技术实时采集目标场景的红外偏振数据,通过深度学习与分焦平面偏振成像紧密融合,实现高质量偏振图像恢复与典型场景下运动目标的智能感知。     

应用残差总变分和低秩表示的视频去噪算法

为了有效地去除视频当中的高斯噪声和脉冲噪声,提出了一种新的视频去噪算法。该算法通过相似图像块组内的残差值总变分及低秩表示来同时探索图像块内的局部相似性以及图像块之间的相似性。首先,采用块匹配的方式在含噪视频中寻找最相似图像块并组合成图像块组;其次将每个相似图像组表达为一个低秩矩阵及一个稀疏矩阵之和,并同时强调低秩矩阵内的残差总变分范数最小化;最后,通过求解最优化问题获得最终的低秩矩阵,即恢复出的图像块组数据。实验结果表明,本文的算法能够有效去除视频当中含有的高斯噪声和脉冲噪声。与同类算法相比,能够获得显著的峰值信噪比提升。      

保信去噪在图像预处理中的应用

在图像去噪处理算法中,一般的方法对于边缘信息总是存在不同程度的信息丢失.本文考虑了一种新的去噪处理的方法--保信去噪.该方法在均值去噪基础上,对其所求的均值求导,判断其均差来确定该点的像素值.此方法有效的克服了均值去噪中图像的边缘模糊的缺点,具有较好的鲁棒性.试验结果表明:该方法的效果明显.     

彩色分焦平面偏振图像配准方法北大核心CSCD

彩色分焦平面偏振相机是一种新型偏振成像装置,能够同时获取目标0°,45°,90°和135°四个偏振方向的彩色三通道(RGB)强度信息,但需要解决分辨率损失和像元错位的问题。本文提出一种适用于分焦平面相机的彩色偏振图像配准方法。通过实验对所提配准方法进行验证,结果表明:所提配准方法能够降低像元错位问题对后续彩色偏振图像解析精度的影响。     

基于三维滤波的视频去噪算法研究

视频去噪的目的是将原始视频从观测到的含噪视频中还原出来。对基于三维滤波的视频去噪算法进行了研究。首先利用贝叶斯阈值对视频序列的各帧在小波域中滤波,之后对帧间连续三帧图像进行帧间滤波。仿真结果表明该算法的有效性。     

用于偏振成像的液晶微偏振阵列研究进展

微偏振阵列是分焦平面偏振计的核心元件.相较于纳米线栅阵列,液晶偏振阵列有着设计灵活、工艺简便、性能稳定等独特优势,有望实现高性价比、高精准度的偏振测量与成像.本文综述了近年来国内外科研团队针对液晶微偏振阵列展开的研究,系统阐述了基于宾主型、偏振旋转型和相位延迟型3大类液晶偏振阵列的器件结构和工作原理,不同的液晶排列结构...     

PCA-based approach for video scene change detection on compressed video

An automatic, real-time detection approach to video scene change detection is presented. Owing to the high correlation of two consecutive video frames, it is proposed that only the eigenvector corresponding to the largest eigenvalue is retained in the principal component analysis (PCA) for video data. A one-dimensional PCA feature of video data is then generated from the PCA. It shows superior performance compared to the histogram feature and the pixel feature. The detection algorithm based on this PCA feature is then designed to detect both abrupt and gradual transitions. The proposed approach is tested on the TREC video test repository to validate its performance     

Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

微偏振片阵列成像的非均匀校正研究

针对传统的非均匀校正算法难以校正偏振成像的非均匀问题，提出了一种新的矩阵校正算法。分析了偏振成像与非偏振成像的非均匀性的不同表现，阐述了微偏振片阵列成像的非均匀产生机理，指出了采用非偏振成像非均匀校正方法的失效原因。在构建偏振成像系统对入射偏振光源的响应模型基础上，提出了矩阵校正法。实验部分给出了矩阵校正法对均匀偏振场本底图像和信息丰富场景图像的校正效果，定量分析结果表明，矩阵校正法将均匀本底图像的非均匀性降至校正前的10%左右。     

PCA-based magnification method for revealing small signals in video

Video magnification techniques are useful for visualizing small changes in videos. Current methods are mainly applied for two aspects: motion amplification and color amplification. For instance, Eulerian video magnification (EVM) has shown impressive results in the context of color of human face and subtle head motion caused by the influx of blood at each beat. Such visual results have possible applications in non-contact human physiological parameter measurement, such as heart rate estimation. Unfortunately, the linear EVM is sensitive to noise and frequencies of the changes should be customized, which generates a limitation of applications. This paper presents an advanced EVM for magnifying the signal amplitude in the presence of relatively high noise as well as unknown the frequencies of changes in video. Principal component analysis (PCA) is performed to decompose the frames and the component whose spatial variation best matches small changes to be magnified. The advantage of PCA-based method is that it can select the subtle signals with a denoising process like spatial filtering. Experimental results show that the PCA-based EVM can support larger amplification factors for small changes visualization as well as less noise and artifacts.     

Motion-compensated temporal filtering for denoising in video encoder

A DCT-domain temporal filtering in a video encoder is presented. It is proven that the multiplication of every DCT coefficient in an inter block with a proper weight is equivalent to motion-compensated temporal filtering in the spatial domain for the inter block. Thus, a temporal filtering in the DCT-domain is proposed, which determines properly the weight by using an effective noise estimation scheme.     

语音去噪LMS自适应滤波器算法的改进

对LMS自适应算法进行了详细的性能分析与讨论,针对LMS算法运算较复杂、适应性较弱、稳定性差的缺点提出了一种HLMS(混合LMS)算法.建立了自适应噪声抵消系统,利用MATILAB软件对食堂、体育馆两处的录音信号进行计算机语音去噪仿真分析.实验结果表明,两种自适应方法均能有效抑制各种噪声污染,提高语音信噪比为60%～8...     

一种空间自适应小波门限去噪算法

提出了一种空间自适应小波门限去噪算法,该算法在小波域对含噪小波系数做两次自适应去噪,两次自适应门限分别基于最大似然(ML)方差估计和最大后验概率(MAP)方差估计.仿真结果表明,该算法与其它自适应门限去噪算法相比,去噪后的图象具有更高的峰值信噪比(PSNR).     

Forward-and-backward diffusion processes for adaptive image enhancement and denoising

Signal and image enhancement is considered in the context of a new type of diffusion process that simultaneously enhances, sharpens, and denoises images. The nonlinear diffusion coefficient is locally adjusted according to image features such as edges, textures, and moments. As such, it can switch the diffusion process from a forward to a backward (inverse) mode according to a given set of criteria. This results in a forward-and-backward (FAB) adaptive diffusion process that enhances features while locally denoising smoother segments of the signal or image. The proposed method, using the FAB process, is applied in a super-resolution scheme. The FAB method is further generalized for color processing via the Beltrami flow, by adaptively modifying the structure tensor that controls the nonlinear diffusion process. The proposed structure tensor is neither positive definite nor negative, and switches between these states according to image features. This results in a forward-and-backward diffusion flow where different regions of the image are either forward or backward diffused according to the local geometry within a neighborhood.     

PSO-based learning of sub-band adaptive thresholding function for image denoising

In this paper, a new computationally efficient approach has been proposed for denoising the images which are corrupted by Gaussian noise. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) technique have been proposed for learning the parameters of adaptive thresholding function required for optimum performance. The proposed PSO-based denoising approach not only speeds up the optimization but also improves the performance in comparison with wavelet transform-based thresholding neural network (WT-TNN) approach. The results obtained shows better edge preservation performance with bior6.8 wavelet filter when compared to db8 wavelet filter. Further, problem of dependency of learning time on initial value of thresholding parameters and noise level in the image have been sorted out in the proposed approach.