Constant time joint bilateral filtering using joint integral histograms

In this brief, we present a constant time method for the joint bilateral filtering. First, we propose an image data structure, coined as joint integral histograms (JIHs). Extending the classic integral images and the integral histograms, it represents the global information of two correlated images. In a JIH, the value at each bin indicates an integral determined by the two images. Then, the joint bilateral filtering is transformed to computation and manipulation of histograms. Utilizing the JIHs, we are capable of joint bilateral filtering in constant time. Its performance is validated in a digital photography approach using Flash-noFlash image pairs. Compared with the brute-force method, the proposed method achieves a speedup factor of 2-3 orders of magnitude while producing similar filtering results.     

CPR artifact removal from human ECG using optimal multichannel filtering

The purpose of this study was to assess whether the artifacts presented by precordial compressions during cardiopulmonary resuscitation could be removed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial compressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation we started with noise-free human ECGs with ventricular fibrillation (VF) and ventricular tachycardia (VT) records. To simulate a realistic resuscitation situation, we added a weighted artifact signal to the human ECG, where the weight factor was chosen to provide the desired signal-to-noise ratio (SNR) level. As artifact signals we used ECGs recorded from animals in asystole during precordial compressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such reference signals are available and, using an adaptive multichannel Wiener filter, we construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the proposed method is demonstrated through graphic examples, SNR, and rhythm classification evaluations.     

SAR image despeckling via bilateral filtering

Bilateral filtering (BF) can realise both smoothing images and preserving edges, whereas its filtering results are always influenced since its two parameters are difficult to configure to the optimum. In this reported work, the application of BF is extended to synthetic aperture radar (SAR) image despeckling, and the despeckling evaluation indexes, including the equivalent number of looks and the edge save index, are used to estimate the parameters. After BF with estimated parameters imposed on a normalised SAR image, further processing can achieve both despeckling and edge preservation simultaneously. Experimental results show that the visual quality and evaluation indexes of the proposed algorithm outperform the classical Lee filtering.     

红外图像的自适应混合双边滤波算法

针对红外图像中的混合噪声,提出了一种自适应混合双边滤波算法。首先对双边滤波原理进行了分析,提出不能滤除强高斯噪声和脉冲噪声是由于双边滤波引入灰度域权值而带来的固有不足,因此根据双边滤波算法的特点设置了一种像素间的相似度,并以该相似度为基础将双边滤波不能滤除的强噪声点进行了标记,仅对红外图像中标记出的强噪声点进行中值滤波以减少图像模糊,对普通噪声点采用灰度方差自适应双边滤波以保留更多边缘特征。自适应混合双边滤波能够有效滤除红外图像中的高斯噪声、脉冲噪声以及由其组成的混合噪声,同时在滤波过程中并不降低双边滤波保留红外图像边缘特征的性能。仿真实验结果表明,与传统双边滤波、改进的双边滤波以及各项异性扩散-中值滤波算法相比,该算法无论是滤除红外图像的混合噪声还是保留边缘特征都较为优越。     

一种自适应双边滤波点云去噪算法

目前双边滤波算法的空间方差σ_s和灰度方差σ_r凭借经验预先设定参数。然而,人工设置参数往往取值不合理,以致出现噪声不能滤除或者不同区域存在降噪和保留边缘特征之间矛盾的现象。文中提出一种自适应参数选择的双边滤波点云去噪算法,该算法首先为每个采样点建立k-邻域,然后以该点处的微切平面为视平面,在该视平面上利用目标尺度信息实现空间方差σ_s局部自适应取值,从而实现双边滤波算法自适应以保留更多的边缘特征。实验结果表明,所提出的方法滤波效果较传统双边滤波在边缘特征细节失真方面明显提高。     

基于改进双边滤波的皮革缺陷检测

为了提高皮革缺陷检测效率, 提出了一种基于改进双边滤波的皮革缺陷检测算法。通过搭建机器视觉检测平台, 完成不同种类缺陷的皮革样本的图像采集, 采用改进的双边滤波算法处理样本图像, 模糊皮革背景纹理并保留缺陷边缘轮廓, 在此基础上, 计算各类缺陷的4种特征参量作为输入向量, 构建了最小二乘支持向量机自动识别模型。结果表明, 与聚类分析算法、阈值分割法和小波分析法相比, 本文中采用的算法能更高效地检测出皮革多种缺陷, 检测平均用时0.83s, 缺陷检测准确率为93.3%。此研究结果为皮革的实时检测提供了有效途径。     

基于亮度不变特征的自适应双边滤波算法

针对目前滤波算法存在的亮度敏感性不足,提出基于亮度不变特征的自适应双边滤波算法.算法首先利用局部相位的最大矩和最小矩来建立空间参数的角点和边缘信息表示,接着采用灰度均一性测度来建立亮度距离参数与图像内在噪声的关系,从而建立双边滤波的自适应参数调整策略.实验结果表明,提出算法的平均峰值信噪比值比已有算法高0.49～2.2 dB,具有更好的图像视觉质量和边缘保持能力.     

基于双边滤波与非局部均值的图像去噪研究

提出了一种基于双边滤波与非局部均值的图像去 噪算法,近年来提出的非局部均值算法是去噪效果非常 出色的算法之一,双边滤波去噪算法采用空间邻近度和灰度相似性构造新的权重系数,其取 得了良好的去噪效果,本文 据此改进非局部均值算法的权重部分,把空间邻近度因子与非局部均值的权重系数相结合, 构造新的权重系数。实验表 明,本文改进权重的非局部均值算法与已有的去噪方法相比,能得到更好的峰值信噪比,能 更好的保护图像细节以及结构信息。     

基于GPU加速的各向异性双边滤波图像抽象化绘制算法

本文提出基于GPU加速的图像及视频的实时抽象化绘制算法。首先通过运用Kuwahara滤波实现图像的颜色特征快速聚类,其次采用各向异性双边滤波算法对图像沿结构张量场进行平滑,从而获得连续的、局部区域色彩一致的结果图像,实验表明该算法简单、易于实现。     

结合全变分的双边滤波图像去噪方法研究

图像去噪是数字图像处理过程中的一个重要步骤,它将直接影响到图像处理的最终质量。针对传统的全变分(TV)正则化去噪算法容易产生阶梯效应的缺点,利用双边滤波去噪算法在空间域和值域两个方面进行滤波的特点,提出一种结合TV模型的双边滤波方法,该方法能在一定程度上有效地改善阶梯效应。仿真实验结果表明,提出的去噪方法不仅能够获得较好的去噪效果,还能有效地保持图像的边缘特征信息,降噪效果明显。在较高水平噪声情况下,与TV算法相比,该方法针对小尺寸灰度图片(256×256)图像的峰值信噪比(PSNR)提高1.45 d B左右,大尺寸灰度图片(512×512)图像的PSNR提高2.56 d B左右。     

Log filtering using gyrators

A general realisation for gyrators applicable to log filters, and ESS filters in general, is proposed. An implementation based on the `E-cells' is given. Its large signal behaviour is shown to be linear by embedding the proposed topology into a first order log filter circuit used to produce a first order highpass filter function in an equivalent way to an R-L filter     

Color image noise removal algorithm utilizing hybrid vector filtering

This paper proposes a robust approach to color image noise removal that efficiently eliminates noisy pixels by exploiting several vector-class characteristics of multichannel pixels. This algorithm treats multichannel images as a vector class and takes both magnitude and phase angles of the pixel vectors into consideration. It consists of two steps: an efficient noise detector based on pixel vector angle statistics and impulse noise filtering with a hybrid of vector magnitude and vector angle function. Extensive experimental results demonstrate that the proposed approach significantly outperforms several other well-known techniques for color image noise removal.     

Transversal filtering using charge-transfer devices

Techniques are presented for making transversal filters using charge-coupled devices and bucket-brigade devices. In a CCD transversal filter, the delayed signals are sampled by measuring the current flowing the clock lines during transfer, and the sampled signals are weighted by a split electrode technique. In a BBD transversal filter, the delayed signals are `tapped' with a source follower whose load determines the weighting coefficient. Examples are given of CCD and BBD filters that are `matched' to particular signalling waveforms, and the limitations of charge-transfer devices in matched filtering applications are discussed. Finally, the application of CTD transversal filters to other signal processing functions is discussed.     

Super-resolution reconstruction using spatio-temporal filtering

It has been known for some time that temporal dependence (motion) plays a key role in the super-resolution (SR) reconstruction of a single frame (or sequence of frames). While the impact of global time-invariant translations is relatively well known, the general motion case has not been studied in detail. In this paper, we discuss SR reconstruction for both motion models from a frequency-domain point of view. A noniterative algorithm for SR reconstruction is presented using spatio-temporal filtering. The concepts of motion-compensated windows and sinc interpolation kernels are utilized, resulting in a finite impulse response (FIR) filter realization. In the simulations, we assume a priori knowledge of the motion (optical flow), which is commonly done throughout much of the SR reconstruction literature. The proposed process is localized in nature, and this enables the selective reconstruction of desired parts of a particular frame or sequence of frames.     

Digital filtering using polynomial transforms

We define discrete transforms in a ring of polynomials. These polynomial transforms have the circular convolution property and can be used for the fast computation of 2-dimensional cyclic convolutions. This yields efficient algorithms for the implementation of 1- and 2-dimensional digital filters.     

Digital filtering using logarithmic arithmetic

A method of computation is described in which all signals are encoded logarithmically, giving a great improvement in dynamic range compared with fixed-point linearly encoded arithmetic. Two ways of adding or subtracting logarithmically encoded numbers are suggested, together with a logarithmic digital-analogue convertor.     

Low-power digital filtering using approximate processing

We present an algorithmic approach to the design of low-power frequency-selective digital filters based on the concepts of adaptive filtering and approximate processing. The proposed approach uses a feedback mechanism in conjunction with well-known implementation structures for finite impulse response (FIR) and infinite impulse response (IIR) digital filters. Our algorithm is designed to reduce the total switched capacitance by dynamically varying the filter order based on signal statistics. A factor of 10 reduction in power consumption over fixed-order filters is demonstrated for the filtering of speech signals     

Colour filtering using local density information

A multichannel filtering technique is proposed based on local density estimation. The window data are ordered according to their distance from that of the maximum density, and the samples to be averaged are selected using the Fisher discriminant criterion. The method has been evaluated in several real images     

Adaptive bilateral filter for sharpness enhancement and noise removal.

In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.     

Blind multichannel identification using overlap-save filtering

An improved cross-relation (CR)-based blind multichannel estimator using the overlap-save filtering technique is proposed. The proposed method performs as well as the original CR method, but is implementationally more efficient.