An adaptive window mechanism for image smoothing

Image smoothing using adaptive windows whose shapes, sizes, and orientations vary with image structure is described. Window size is increased with decreasing gradient magnitude, and window shape and orientation are adjusted in such a way as to smooth most in the direction of least gradient. Rather than performing smoothing isotropically, smoothing is performed in preferred orientations to preserve region boundaries while reducing random noise within regions. Also, instead of performing smoothing uniformly, smoothing is performed more in homogeneous areas than in detailed areas. The proposed adaptive window mechanism is tested in the context of median, mean, and Gaussian filtering, and experimental results are presented using synthetic and real images and compared with a state-of-the-art method.     

A novel adaptive SVR based filter ASBF for image restoration

In this paper, a novel adaptive filter ASBF based on support vector regression (SVR) is proposed to preserve more image details and efficiently suppress impulse noise simultaneously. The main idea of the novel filter ASBF here is to employ a SVR based impulse detector to judge whether an input pixel is contaminated or not by impulse noise. If this case happens, a median filter is employed to remove the corresponding impulse noise. This judgment procedure is executed by regressing the filter window of an input pixel using SVR and then judging the input pixel by its regression distance. Huber loss function is used in SVR regression, due to its excellent robustness capability. The distinctive advantage of the filter ASBF over the latest Support Vector Classifier (SVC) based filter is that no training for the original noise-free image is required in our approach, which is well in accordance with our visual judgment way. Experimental results for benchmark images demonstrate that our filter ASBF here outperforms the extensively-used median-based filters and the SVC based filter.     

一种异性扩散的自适应平滑滤波算法

固定尺度的高斯滤波器对图像中的所有像素使用相同的加权系数,在平滑噪声的同时也模糊了边缘信息,影响了后期的边缘定位和提取,为此提出了一种异性扩散的自适应仿高斯滤波器,通过计算当前像素与周围像素的灰度梯度,预判该像素类型（噪声点或是边缘点）,并根据像素点类型和梯度生成自适应的、各向异性的加权系数,通过多次平滑迭代实现权重的异性扩散。实验结果表明,该滤波算法具有平滑噪声的同时保留并锐化边缘效果。     

Graph spectral image smoothing using the heat kernel

A new method for smoothing both gray-scale and color images is presented that relies on the heat diffusion equation on a graph. We represent the image pixel lattice using a weighted undirected graph. The edge weights of the graph are determined by the Gaussian weighted distances between local neighboring windows. We then compute the associated Laplacian matrix (the degree matrix minus the adjacency matrix). Anisotropic diffusion across this weighted graph-structure with time is captured by the heat equation, and the solution, i.e. the heat kernel, is found by exponentiating the Laplacian eigensystem with time. Image smoothing is accomplished by convolving the heat kernel with the image, and its numerical implementation is realized by using the Krylov subspace technique. The method has the effect of smoothing within regions, but does not blur region boundaries. We also demonstrate the relationship between our method, standard diffusion-based PDEs, Fourier domain signal processing and spectral clustering. Experiments and comparisons on standard images illustrate the effectiveness of the method.     

Edge-preserving smoothing using a similarity measure in adaptive geodesic neighbourhoods

This paper introduces a novel image-dependent filtering approach derived from concepts known in mathematical morphology and aiming at edge-preserving smoothing of natural images. Like other adaptive methods, it assumes that the neighbourhood of a pixel contains the essential information required for the estimation of local features in the image. The proposed strategy essentially consists in a weighted averaging combining both spatial and tonal information. For that purpose, a twofold similarity measure is calculated from local geodesic time functions. This way, no prior operator definition is required since a weighting neighbourhood and a weighting kernel are determined automatically from the unfiltered input data for each pixel location. By designing relevant geodesic masks, two adaptive filtering algorithms are derived that are particularly efficient at smoothing heterogeneous areas while preserving relevant structures in greyscale and multichannel images.     

一种改进的自适应中值滤波方法

提出了一种改进的自适应中值滤波算法,以有效地去除图像中的脉冲噪声,并保留图像细节。在进行噪声点检测时,引入了最小集合距离测度,有效地避免了将高频细节信号误判为噪声。采用最小无污染点集合的中值恢复噪声点,消除了其邻域噪声点的影响。通过与RAMF、NASMF等方法的比较实验表明,新算法噪声检测的正确率高、降噪与保留细节效果好, 尤其对含噪声密度高的图像的处理效果优势更为明显。     

An effective edge-preserving smoothing method for image manipulation

This paper presents a novel and effective edge-preserving image smoothing method for edge-aware image manipulation. The method formulates the smoothing as a problem of minimizing a convex object function with a constraint and an efficient solution to the optimization problem is presented. Specifically, the method provides an unified framework to regularize the edge and texture pixels in the optimization so that geometric edges representing image structures can be well retained and fine edges of texture regions are removed or suppressed. Both qualitative and quantitative experimental results on natural images and computer-generated structured images have shown the efficacy of the proposed method. In addition, the proposed method can improve the performance of many image processing and manipulation tasks including edge extraction and simplification, non-photorealistic rendering, detail and contrast exaggeration, HDR tone mapping, block-based discrete cosine transform (BDCT) artifact removal and content-aware image resizing, as demonstrated through the experiments.     

基于matlab的图像增强技术分析与实现

图像增强是图像处理的一个重要分支，是图像边缘提取、图像分割等的基础。由于图像在获取和传输过程中发生失真，影响了人和机器对图像的理解。文章主要介绍了常见的空域处理法。通过实验的对比，发现图像得到了很好的增强效果。     

基于matlab的图像增强技术分析与实现

图像增强是图像处理的一个重要分支,是图像边缘提取、图像分割等的基础。由于图像在获取和传输过程中发生失真,影响了人和机器对图像的理解。文章主要介绍了常见的空域处理法。通过实验的对比,发现图像得到了很好的增强效果。     

Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. This work performs a predictive study of the principal index of the Brazilian stock market through artificial neural networks and the adaptive exponential smoothing method, respectively. The objective is to compare the forecasting performance of both methods on this market index, and in particular, to evaluate the accuracy of both methods to predict the sign of the market returns. Also the influence on the results of some parameters associated to both methods is studied. Our results show that both methods produce similar results regarding the prediction of the index returns. On the contrary, the neural networks outperform the adaptive exponential smoothing method in the forecasting of the market movement, with relative hit rates similar to the ones found in other developed markets.     

A new class of edge-preserving smoothing filters

A new class of edge-preserving noise-cleaning filters are introduced which use both spatial and nearest-neighbor constraints on image pixels to smooth an image. They are simple, fast and good at preserving edges and thin structural details in images. Edges and corners of varying contrasts that may be present in real images have been simulated in synthesized random checkerboards. The performance of the new smoothing filters on the blurred and noisy checkerboards has been quantitatively compared with that of four other filters.     

基于纹理分析的保细节平滑滤波器

平滑去噪是图象处理中一个重要课题,但是以往在处理平滑去噪问题上一直存在平滑和保细节的矛盾。为解决此问题,提出了一种基于纹理分析和保细节平滑滤波器,该滤波器采用了多尺度多方向的模板,并利用纹理分析等手段,同时根据图象各部分特性,通过自适应地选择模板来进行平滑滤波,该算法兼顾了降噪和保细节两方面要求。实验结果证明,该算法实现简单,计算速度快,且效果优于其他几种常用的保边界平滑算法。     

自适应平滑自商图与多层分块的人眼定位算法

针对光照对眼睛定位产生的影响,提出了一种基于自适应平滑自商图像与多层分块的人眼定位算法。首先进行光照评估,利用自适应平滑自商图像（ASSQI）对光照条件差的进行处理,消除人脸图像的光照影响;然后运用眼睛区域梯度复杂度大的特征并结合积分图分块处理获得眼睛粗定位;最后对粗定位区域继续分块并利用质心得到准确的瞳孔中心。在JAFFE数据库、Yale B数据库和BioID数据库上测试表明该文算法有较高的定位正确率,对光照的鲁棒性较强。     

Easy recipes for cooperative smoothing

In this paper we suggest how several competing signal smoothers, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable smoothing algorithm. The proposed heuristic, but statistically well motivated, fusion mechanism allows one to combine practically all kinds of smoothers, from simple local averaging or order statistic filters, to parametric smoothers designed for different hypothetical signal and/or noise models. It also allows one to account for the distribution of measurement noise, and in particular to cope with heavy-tailed disturbances, such as Laplacian noise, and light-tailed disturbances, such as uniform noise.     

一种基于自适应阈值的保细节平滑滤波器

基于多尺度多方向的模板,提出一种自适应地的细节平滑算法.该算法一方面保持了模板在保留图像细节特征方面的优势,另一方面利用图像灰度梯度直方图的统计特征,将整幅图像分割成若干子图像,再根据各个子图像的特性,并结合全局梯度特征信息自适应地生成动态阈值,有效地解决了以往阈值选取的困难,提高了图像平滑的自动化程度.与现有的其他算法相比,该算法实现简单,计算速度快,在实际应用中取得了很好的效果.     

各向异性扩散的彩色图像复合滤波方法

将扩散偏微分方程PDE’s推广到彩色图像滤波。分析了基于散度算子和基于迹算子PDE的优缺点,提出了一个新的扩散PDE方法。该方法在各向异性扩散基础上增加了一个震动滤波算子,并对特征根和扩散张量进行了重构,不仅对图像有很好的滤波效果,而且对边缘有增强保护的作用。实验结果表明,提出的方法达到了既有效去除噪声又保留更多细节的目的。     

Sugeno integral generalization applied to improve adaptive image binarization

Classic adaptive binarization methodologies threshold pixels intensity with respect to adjacent pixels exploiting integral images. In turn, integral images are generally computed optimally by using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images. Which, in turn, this technique is supported by an efficient design of a modified SAT for generalized Sugeno fuzzy integrals. We define this methodology as FLAT (Fuzzy Local Adaptive Thresholding). Experimental results show that the proposed methodology produced a better image quality thresholding than well-known global and local thresholding algorithms. We proposed new generalizations of different fuzzy integrals to improve existing results and reaching an accuracy $\approx 0.94$ on a wide dataset. Moreover, due to high performances, these new generalized Sugeno fuzzy integrals created ad hoc for adaptive binarization, can be used as tools for grayscale processing and more complex real-time thresholding applications.     

自适应图像复原模型梯度阈值选取研究

自适应图像复原模型,能满足中高噪声强度下进行图像复原的要求,但在选取图像梯度阈值时,通过交互迭代,运算量大,耗费时间长。本文基于对图像边缘灰度突变特性的考虑,通过引入图像梯度幅值直方图的方法,利用像素点梯度模值来区分边缘区和平滑区,开辟了在图像复原中选取梯度阈值参数的新途径,取得了很好的降噪复原效果,而且,选取梯度阈值的过程,耗时少,运算简单,选取过程直观快捷。     

十字基自适应指导滤波在图像降噪中的应用

针对于传统线性滤波器在非边界区域与边界区域有着滤波平滑和边缘保护的矛盾,提出了一种基于指导滤波和十字基相结合的算法。其主要思想是对指导滤波加入自适应偏移量进行调整,保证在边缘结构处细节的保留;普通指导滤波的计算邻域为固定大小的方形窗,在自适应指导滤波算法邻域窗的选取中引入十字基滤波窗,以灰度相似性作为阈值,利用立体匹配的优势,生成大小可自动调节、形状可自适应调整的滤波块,从而实现了滤波性能的提高;偏移量的选取值随滤波窗灰度阈值正比变化,比硬阈值有更好的效果。通过对国际标准图像序列的实验仿真结果比较,表明十字基滤波窗在分块方面有着较高的效率,平滑区域的滤波效果更佳,偏移量的加入使算法在边界区的纹理和细节特征得以保留;较之普通指导滤波算法,提出的十字基自适应指导滤波在峰值信噪比(PSNR)值上提高了约2 dB;通过对实拍图像的处理结果对比表明该算法在实际运用中有着更好的适用性。     

自适应窗宽的均值迁移图像跟踪算法

针对传统均值迁移跟踪算法中核函数窗宽固定使之无法满足图像中运动目标尺寸变化需要的问题，在分析目标特征零阶矩特点基础上，以其变化比作为观测量，以目标面积的变化比作为状态量，利用卡尔曼滤波器对未来帧中目标面积的变化进行预测进而获得同目标尺度变化相适应的核函数窗宽。该算法通过窗宽自适应变化，提高了跟踪精度，增强了跟踪稳定性，同时仍保证了跟踪的实时性。实验结果证明了该方法的有效性。