基于多方向中值滤波的各向异性扩散滤波算法

提出一种改进各向异性扩散滤波算法。现有研究方法多存在图像边缘不清,误识别多,扩散系数多凭主观选择等问题。该算法利用保留细节和边缘的能力较为突出的多方向中值滤波方法在多个方向上进行扩散,利用局部方差和图像梯度改进了扩散系数,通过多次迭代修正扩散系数,增强了算法的鲁棒性,且在滤除噪声的同时注重对边缘细节的保持。通过具体实验仿真,以峰值信噪比、均方误差、结构相似度以及图像佳数4个参数作为指标,对实验仿真结果进行了量化比较,表明该算法与传统各向异性扩散方法以及Catté_PM模型等改进方法相比,具备更好的滤除图像噪声以及保持图像边缘的能力。     

结合邻域方差和各向异性窗的引导滤波算法EI北大核心CSCD

针对引导滤波会导致边缘附近出现光晕且难以识别精细边缘的问题,提出了一种结合邻域方差与各向异性窗的引导滤波算法.首先,利用各向异性高斯滤波器的方向选择性实现对边缘的精细识别,并利用滤波器的狭长空域结构可实现局部窗口内不同像素信息融合,以抑制边缘模糊和光晕效果;其次,基于局部结构相似性原理,引入邻域方差以实现对局部线性变换参数的优化,同时保证强边缘结构和非边缘区域的最大扩散.实验结果表明,在102类花卉图像数据集上,文中算法的视觉效果、定量评价(PSNR和SSIM)均优于其他边缘保持滤波算法,并且测试图像的失真度比引导滤波、加权引导滤波和各向异性引导滤波分别小46.72%,48.64%和29.61%,能够在识别精细边缘的同时有效地抑制伪影现象的发生.     

基于图像局部几何结构的SAR图像降噪与增强*

研究了基于图像局部几何结构对SAR图像进行各向异性扩散滤波降噪。首先,回顾各向异性扩散滤波的PM模型、Weickert模型和Tschumperle的迹模型,分析指出迹模型能够依据图像局部几何结构进行定向扩散滤波且扩散程度由扩散率函数决定,扩散过程可控,意义直观;继而,根据扩散系数的构建原则,构建了新的兼容图像增强、扩散幅度可调整的扩散率函数,并用于SAR图像降噪。实验结果表明,运用此函数不仅有效抑制了相干斑噪声,还保持并增强了边缘细节,取得了理想的效果。     

基于尺度空间理论和反扩散函数的图像去噪

在使用扩散过程平滑噪声之后引入反扩散过程来恢复边缘，结合尺度空间理论和反扩散函数对图像进行去噪处理。该方法使用最小描述长度（MDL）准则自适应地选择图像中每一点处的最优尺度对图像进行滤波。加入尺度范围限制降低了过平滑和欠平滑的影响。改进了反扩散函数模型，对降质图像中的边缘进行恢复。与经典的滤波方法以及各向异性扩散方程的结果相比。本文方法取得了较好的效果。     

各向异性滤波算法在地震曲率属性中的应用

在曲率属性计算之前需要对图像进行去噪预处理,传统的图像滤波方法在去除噪声的同时会破坏边缘、线条、纹理等图像特征,而基于偏微分方程的P-M模型在平滑过程中会出现块效应.针对这些问题,提出了一种基于张量扩散的各向异性滤波的预处理方法.通过定义散布矩阵来获得丰富的图像局部结构信息,然后利用这些结构来控制扩散过程,以便实现图像的更好滤波.理论分析和实验结果表明,相较于一些常规的图像滤波算法,各向异性滤波得到的曲率属性效果更清晰、质量更高.     

各向异性扩散与高斯差分模型的多聚焦图像融合方法

利用各向异性扩散模型具有良好的边缘保持特性,提出一种基于各向异性扩散滤波与高斯滤波差分规则的图像融合算法。各向异性扩散方程对图像进行滤波操作,在图像的同质区域实施正向扩散以平滑图像,而在图像边缘实行较弱平滑以保护边缘细节信息。将通过各向异性扩散模型处理的图像与经过高斯函数滤波的结果图像进行差分操作,可以得到图像的高频系数信息。为提高健壮性,对高频系数进行小窗口累加,其作为像素选择准则,再分别从原始图像中直接获取对应的像素值组成融合结果图像。实验结果表明,所提出的方法可以有效地融合源图像信息,非常适合多聚焦     

基于图像特征方向的各向异性扩散滤波方法

传统的各向异性扩散滤波方法都是从偏微分方程本身出发的,理论上的分析较为复杂.本文研究了基于图像特征方向的内在正交坐标系,分析了在此框架下的扩散滤波机制,然后直接从该坐标系下建立各向异性扩散滤波方案.这样的扩散滤波方法更加直观,可以简化理论分析.在此框架下,提出了一种新的各向异性扩散滤波方法.数值实验结果表明,新的扩散滤波方法可以更好地考虑图像的局部特性,从而完成细节保护和噪声消除的双重功能.所以,基于图像特征方向建立的各向异性扩散滤波方法更能达到我们预期的效果,该设计方法是有效的.     

基于拓扑中值滤波的红外图像小波边缘提取

分析了边缘类型对小波多尺度边缘提取的影响.针对红外目标图像对比度低、噪声大、灰度缓慢变化的特点,在进行小波变换前利用拓扑中值滤波法的边缘象素定位精度高的特性,滤除噪声并初步定位大部分边缘象素.经试验此方法可明显提高红外图像的小波多尺度边缘提取的质量.     

基于Canny算子的边缘切线扩散医学超声图像去噪

医学超声图像的细节特征在临床诊断中具有重要的意义.针对于传统的PM算法以及各种改进型各向异性去噪方法(Catte_PM、SRAD、CENCD等)存在边缘中的噪声点未作处理,多次迭代产生虚假边缘等缺点,通过分析具有代表性的Catte_PM各向异性模型,提出了一种结合自适应Canny算子,沿图像边缘切线方向扩散的去噪方法.该算法首先通过改进的Canny算子将图像范围分为边缘区和非边缘区;其次改进现有的扩散方法,使扩散方向只沿图像边缘切线方向进行;最后对非边缘区域采用有限次(三次)的各向同性滤波.实验结果表明,该方法能够有效地解决滤波和图像细节保护这一矛盾问题,使得图像质量有较明显的改善.     

联合结构张量和变指数正则变分医学图像复原

利用图像局部特征,提出了一种基于[Lp]范数的变指数正则变分模型。采用结构张量作为[Lp]范数算子的自适应调整参数,克服了传统算子对噪声敏感的缺陷。从扩散的角度看,该模型是各向异性的,在图像同质区趋于平滑滤波,在图像渐变区趋于沿边缘方向扩散。该方法在扩散的同时更好地保持图像的边缘细节。实验结果表明,该方法对医学图像的复原效果优于其他几种变指数变分模型,各种客观性能指标也更佳。     

Tensor scale: A local morphometric parameter with applications to computer vision and image processing

Scale is a widely used notion in image analysis that evolved in the form of scale-space theory whose key idea is to represent and analyze an image at various resolutions. Recently, the notion of localized scale—a space-variant resolution scheme—has drawn significant research interest. Previously, we reported local morphometric scale using a spherical model. A major limitation of the spherical model is that it ignores structure orientation and anisotropy, and therefore fails to be optimal in many imaging applications including biomedical ones where structures are inherently anisotropic and have mixed orientations. Here, we introduce a new concept called “tensor scale”—a local morphometric parameter yielding a unified representation of structure size, orientation, and anisotropy. Also, a few applications of tensor scale in computer vision and image analysis, especially, in image filtering are illustrated. At any image point, its tensor scale is the parametric representation of the largest ellipse (in 2D) or ellipsoid (in 3D) centered at that point and contained in the same homogeneous region. An algorithmic framework to compute tensor scale at any image point is proposed and results of its application on several real images are presented. Also, performance of the tensor scale computation method under image rotation, varying pixel size, and background inhomogeneity is studied. Results of a quantitative analysis evaluating performance of the method on 2D brain phantom images at various levels of noise and blur, and a fixed background inhomogeneity are presented. Agreement between tensor scale images computed on matching image slices from two 3D magnetic resonance data acquired simultaneously using different protocols are demonstrated. Finally, the application of tensor scale in anisotropic diffusive image filtering is presented that encourages smoothing inside a homogeneous region and also along edges and elongated structures while discourages blurring across them. Both qualitative and quantitative results of application of the new filtering method have been presented and compared with the results obtained by spherical scale-based and standard diffusive filtering methods.     

Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor

Super-resolution land-cover mapping (SRM) is a technique for generating land-cover thematic maps with a finer spatial resolution than the input image. Linear mixture model-based SRM (LSRM) is applied directly to a remotely sensed image and is composed of a spatial term that integrates the land-cover spatial pattern prior information, a spectral term that assumes that the spectral signature of each mixed pixel is composed of a weighted linear sum of endmember spectral signatures within that pixel and a balance parameter that defines the weight of the spatial term. The traditional LSRM adopts an isotropic spatial autocorrelation model in the land-cover spatial term for different classes and a fixed balance parameter for the entire image, and ignores the image local properties. The class boundaries are at risk of oversmoothing and may be imprecise, and the homogeneous regions may be unsmoothed and contain speckle-like artefacts in the result. This study proposes a locally adaptive LSRM (LA-LSRM) that integrates image local properties to predict fine spatial resolution pixel labels. The structure tensor is applied to detect the image local information. The LA-LSRM spatial term is locally adaptive and is composed of an anisotropic spatial autocorrelation model in which the spatial autocorrelation orientations of different classes may vary. The LA-LSRM balance parameter is locally adaptive to the different regions of the image. Such parameter obtains a relatively large value when the fine-resolution pixel is located in the homogeneous region to remove speckle-like artefacts and a relatively small value when the fine-resolution pixel is at the class boundary to preserve the edge. The LA-LSRM performance was assessed using a simulated multi-spectral image, an IKONOS multi-spectral image, a hyperspectral image produced by Airborne Visible/Infrared Imaging Spectrometer and a hyperspectral image produced by reflective optics system imaging spectrometer. Results show that the homogeneous regions were smoothed, the boundaries were better preserved and the overall accuracies were increased by LA-LSRM compared with traditional LSRM in all experiments.     

Parallel adaptive sampling and reconstruction using multi-scale and directional analysis

We propose a novel parallel image space adaptive rendering approach. Contourlet transform which includes Laplacian pyramid and directional filter banks is modified for multi-scale and directional analysis in our algorithm. In sampling stage, the image space is coarsely sampled first. The sampled image is analyzed into coarse and difference values in multi-scale using Laplacian pyramid transform. Based on the analysis, a heuristic method is proposed to repeatedly distribute adaptive Monte Carlo samples. In reconstruction stage, the final image is reconstructed by filtering each pixel using our anisotropic per-pixel filter. The filter size depends on the variance and attenuation values. The filter’s anisotropic property is computed by the directional filter banks. Compared to the state-of-the-art image space adaptive rendering methods, the results rendered by our algorithm show improvement in both visual image quality and numerical error while using sparse samples.     

A class of adaptive directional image smoothing filters

The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters.     

方向小波域的选择性阈值SAR图像去噪

SAR图像去噪一直是SAR图像处理中一个具有特殊意义的研究课题。噪声抑制的关键是解决图像平滑与保持纹理之间的矛盾。提出了一种基于方向小波的选择性阈值SAR图像去噪算法。该算法利用方向小波的多方向框架对图像作12个方向的分解和变换。针对方向小波分解图像所产生的系数序列长度不同的特点,利用白噪声的置信区间,将不同长度的系数分成3组,对中间长度的系数序列采用统一阈值,对其他长度序列采用白噪声置信区间阈值处理。为了更好地保持图像细节信息,将每一尺度高频系数的方差中值作为噪声方差估计值。利用真实的SAR图像进行去噪试验,与几种经典的空域滤波和小波软阈值算法进行比较结果表明,该算法在平滑图像的同时更好地保持了图像本身的纹理信息,图像的视觉效果优于其他算法,等效视数和边缘保持指数分别提高了97和0.15。     

基于灰度最值和方向纹理的概率滤波算法

为了更高效地检测和滤除噪声,基于灰度最值和方向纹理的概率滤波算法根据灰度最值进行噪声检测,对检测出来的可疑噪声,根据四个方向纹理的平滑过渡性进行第二次噪声检测。运用滤波窗口中出现频次最高的信号像素取代噪声。如果窗口中不存在信号像素,增大滤波窗口,以使窗口包含信号像素。当滤波窗口增大到允许的最大尺寸时,窗口中依然没有信号像素,则用窗口中已处理的、出现频次最高的像素取代噪声。将算法与当前滤波性能最好的中值滤波算法用于图像滤波实验。从滤波结果的主观视觉效果和客观数据两方面进行的比较分析证明,相对于当前的中值滤波算法,基于灰度最值和方向纹理的概率滤波算法具有更加良好的滤波性能,在滤除噪声的同时,很好地保持图像的边缘和细节。     

Image representation using block pattern models and its imageprocessing applications

An image representation scheme using a set of block pattern models (BPMs) consisting of three categories (constant, oriented, and irregular) is introduced. Algorithms for model classification, model parameter estimation, and image reconstruction from model parameters are presented, and these provide the necessary vehicles for applying the proposed representation scheme to various image processing tasks. The applications of the proposed models in image coding, image zooming, and image smoothing are described. Satisfactory coded images have been obtained at bit rates between 0.5~0.6 b.p.p. (bits per pixel) with a high-rate realization and between 0.3~0.5 b.p.p. with a low-rate realization. The high-rate realization has a simple structure suitable for real-time implementation. The methods for image zooming and smoothing are similar, where both adapt the processing for each pixel according to the model of its neighborhood. By using directional filters in oriented regions, edges and lines are rendered sharper in a smoother manner than with conventional linear filtering approaches, which leads to significant improvement in perceived image quality     

Scale‐adaptive Structure‐preserving Texture Filtering

This paper proposes a scale‐adaptive filtering method to improve the performance of structure‐preserving texture filtering for image smoothing. With classical texture filters, it usually is challenging to smooth texture at multiple scales while preserving salient structures in an image. We address this issue in the concept of adaptive bilateral filtering, where the scales of Gaussian range kernels are allowed to vary from pixel to pixel. Based on direction‐wise statistics, our method distinguishes texture from structure effectively, identifies appropriate scope around a pixel to be smoothed and thus infers an optimal smoothing scale for it. Filtering an image with varying‐scale kernels, the image is smoothed according to the distribution of texture adaptively. With commendable experimental results, we show that, needing less iterations, our proposed scheme boosts texture filtering performance in terms of preserving the geometric structures of multiple scales even after aggressive smoothing of the original image.     

细节感知的纹理滤波算法

目的 基于现有的研究提出一种细节感知的纹理去除算法,在去除图像纹理时,能够很好地保持图像的结构信息,尤其是诸如细长结构和边角信息等在其他方法中容易被模糊化的特殊细节。方法 首先,本文提出一种能够识别细长结构的结构检测方法,对细长结构进行检测并增强其结构特征。其次,为了估计每个像素点的最优滤波核尺度,改进原有的相对总变差模型,多方向寻找最小相对总变差,使它能够更好地区分纹理和边界,并且将边角信息从纹理中区分出来。然后,将检测出来的细长结构归一到改进的相对总变差的度量尺度上,估计滤波核尺度,生成引导滤波图像。这样就能够在平坦或有纹理的区域运用大尺度的滤波核,并在结构边缘和边角附近减小滤波核。最后,通过联合双边滤波器得到纹理去除后的图像。结果 实验测试了马赛克图像和艺术画作,对比了相对总变差和尺度敏感的结构保护滤波等方法,本文方法在去除纹理的同时保留了细长结构和边角细节,并且具有良好的普适性和鲁棒性。利用本文算法处理一幅含10万像素的图像,仅通过一次迭代计算就能够去除大量纹理且效果优于已有的方法,本算法的计算时间为3.37 s,其他算法为0.07~3.29 s。结论 本文设计的纹理滤波器不仅在保持诸如细长结构方面的性能更好,而且使纹理去除后的图像在边角细节处更尖锐,为图像的后续处理提供了一种强有力的图像预处理方式。     

Image and Video Abstraction by Coherence‐Enhancing Filtering

In this work, we present a non‐photorealistic rendering technique to create stylized abstractions from color images and videos. Our approach is based on adaptive line integral convolution in combination with directional shock filtering. The smoothing process regularizes directional image features while the shock filter provides a sharpening effect. Both operations are guided by a flow field derived from the structure tensor. To obtain a high‐quality flow field, we present a novel smoothing scheme for the structure tensor based on Poisson's equation. Our approach effectively regularizes anisotropic image regions while preserving the overall image structure and achieving a consistent level of abstraction. Moreover, it is suitable for per‐frame filtering of video and can be efficiently implemented to process content in real‐time.