连续子邻域内的鲁棒双边滤波

提出一种连续子邻域内的鲁棒双边滤波算法(Robust Bilateral Filtering)。首先,利用自适应区域生长方法在图像局部部域中分割出种子像素的连续子部域;然后,在该连续子部域中采用改进的双边滤波算法对种子像素值进行平滑处理。为了提高算法的鲁棒性能,类似非局域均值滤波算法(Non-Local Means Filtering),以像素空间临近度和像素局部窗口相似度定义该滤波器核函数。算法结合了双边滤波和非局域均值滤波的优点,且在连续子部域内进行去噪处理相对可获得更为合理的图像效果。仿真实验表明,该算法具有良好的去噪效果,同时较好地保留了图像的细节特征。     

Neuro fuzzy and punctual kriging based filter for image restoration

In this paper, we present a hybrid, image restoration approach. The proposed approach combines the geostatistical interpolation of punctual kriging, artificial neural networks (ANNs), and fuzzy logic based approaches. Images degraded with Gaussian white noise are restored by first utilizing fuzzy logic for selecting pixels that needs kriging. Three fuzzy systems are employed. Both type-I and type-II fuzzy sets in addition with neuro fuzzy classifier (NFC) have been used for the detection of noisy pixels. To avoid edge pixels, a post processing technique is used to check the edge pixel connectivity up to lag 5. If the pixel under consideration is an edge pixel, it is excluded from the fuzzy map and thus not estimated. The concept of punctual kriging is then used to estimate the intensity of a noisy pixel. ANN is employed to minimize the cost function of the kriging based pixel intensity estimation procedure. ANN, in contrast to analytical methodologies, avoids both matrix inversion failure and negative weights problems. Image restoration performance based comparison has been made against adaptive Weiner filter and existing fuzzy kriging approaches. Experimental results using 450 images are used to validate the effectiveness of the proposed approach. Different image quality measures are used to compare the efficacy of the proposed NFC and fuzzy type-II approaches for detecting noisy pixels in conjunction with ANN and kriging based estimation.     

基于模糊推理的多窗口中央值滤波器设计方法

提出了一种基于模糊推理用于去除图像椒盐噪声的中央值滤波器的新型设计方法,在图像复原处理中,理想的期望是对图像被劣化的部分处理,没有被劣化的部分不作处理,但实际图像处理中处理点是否为噪声点具有模糊性.利用模糊推理对处理点像素多大程度上属于劣质像素进行推定,并且多个模糊滤波器联合使用,处理结果证明对广范围噪声发生率的各种被椒盐噪声劣化的图像复原处理都适用.     

结合多特征和模糊偏好关系的高分辨率遥感影像分割

高空间分辨率(简称高分辨率)遥感影像除光谱特征外,还包含丰富的纹理特征,为了实现高分辨率遥感影像的高精度分割,提出结合多特征和模糊偏好关系的分割方法.首先,通过像素光谱测度定义多种统计特征,根据定义的各个特征提取特征影像并分别实现影像分割,利用其结果构建模糊决策矩阵;然后,基于像素定义特征间的模糊偏好关系矩阵,计算不同特征对最终分割决策的权重,并对模糊决策矩阵加权以突出优势特征,抑制劣势特征;最后,通过反模糊化决策矩阵得到最优影像分割结果.对合成影像和真实高分辨率遥感影像的分割结果进行定性和定量评价,结果表明,合成影像的分割总精度为99.8%, Kappa值为0.998,说明所提出的算法通过结合各特征的优势部分能够获得高精度的分割结果.     

基于特征组合的人脸跟踪方法

针对复杂背景下的彩色视频序列图像,提出一种基于多特征组合的人脸跟踪方法.该方法采用肤色特征与运动特征来描述视频序列图像中的人脸,分别构造特征似然作为区分人脸目标与背景的置信度,并利用粒子滤波框架原理,用组合的特征似然来表征粒子权重.该方法中提出的自肤色检测算法避免了光线与类肤色像素对肤色特征的影响.在跟踪过程中根据分类...     

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

This paper proposes a multiclass support vector machine (SVM) based adaptive filter for removal of impulse noise from color images. The quality of the image gets degraded due to the presence of impulse noise. As a result, the homogeneity amongst the pixels gets distorted that needs to be restored. The feature set comprising of prediction error, difference between the median value and the center pixel; the median value in the kernel under operation has been used during this study. The pixel of test image is processed using adaptive window based filter that depends on the associated class assigned at the testing phase. The baseline system has been designed using modified histogram based fuzzy color filter (MHFC) technique. Four set of experiments have been carried out on a large database to validate the proposed method. The performance of the technique have been evaluated using peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results suggest that for fixed valued impulse noise, the proposed filter performs better than the MHFC in case of high density impulse noise (>45%). However, for random valued impulse noise the proposed filter outperforms the MHFC based method for both low and high density of noise. The objective analysis suggests that there is ∼3 dB improvement in PSNR as compared to the MHFC based method for high density of impulse noise. The results of SSIM along with visual observations indicate that the image details are maintained significantly in the proposed technique as compared to existing methods.     

Two-Tier genetic programming: towards raw pixel-based image classification

Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately.     

Binarization of degraded document image based on feature space partitioning and classification

In this paper, we propose a new algorithm for the binarization of degraded document images. We map the image into a 2D feature space in which the text and background pixels are separable, and then we partition this feature space into small regions. These regions are labeled as text or background using the result of a basic binarization algorithm applied on the original image. Finally, each pixel of the image is classified as either text or background based on the label of its corresponding region in the feature space. Our algorithm splits the feature space into text and background regions without using any training dataset. In addition, this algorithm does not need any parameter setting by the user and is appropriate for various types of degraded document images. The proposed algorithm demonstrated superior performance against six well-known algorithms on three datasets.     

用于图像恢复的基于SVR的自适应新滤波器的研究*

提出了一种用SVR回归器识别脉冲噪声的思想,并将其应用于图像滤波和恢复,形成了用于对脉冲噪声进行滤波的SVR自适应滤波器。这种滤波器在滤波时,先用SVR对待识别像素作噪声识别,再对含噪声的像素作中值滤波。用SVR作噪声识别时,先对滤波窗口作SVR回归,通过待识别像素回归距的大小判断其是否含有噪声。在进行SVR回归时,使用鲁棒的Huber损失函数。由于更充分地利用了待识别像素点的局部背景信息,这种滤波器提高了脉冲噪声识别的正确率。实验表明,在保留原图像的细节信息方面,其滤波效果要优于基于SVC的中值滤波器。     

A maximum likelihood filter using non-local information for despeckling of ultrasound images

This work presents a new ultrasound image despeckling method based on the maximum likelihood principle that effectively exploits non-local information for estimating noise-free pixels. First, a new maximum likelihood filter is proposed which uses neighborhood information to despeckle images. For this purpose, the general speckle model is used in the log-likelihood function and despeckled pixels are obtained by maximizing this function. Second, the proposed filter is developed to use non-local information such that the distribution of each noisy pixel is weighted according to the statistical distance between the patch of the noisy pixel and that of the target pixel. Because it is optimally designed for ultrasound images, the Pearson distance is used to measure the statistical distance between the patches. A series of experiments are conducted on three different ultrasound images and one synthetic image. Subjective evaluations show that the proposed method is able to preserve edges and structural details of the image and objective evaluations using equivalent number of looks, natural image quality evaluator, peak signal-to-noise ratio, mean preservation, and structural similarity confirm that the proposed method can achieve superior performance.     

基于图像受噪程度的改进模糊加权均值滤波

根据高斯噪声密度大、噪声强度的波动范围宽,其污染图像不仅每一个像素灰度级都会受影响,而且即使是同一灰度级受污染的程度也会不同的特点和传统的图像模糊滤波算法在图像细节保护方面上的不足,提出基于图像受噪程度的改进模糊加权均值滤波算法,该算法根据图像各像素点的受噪程度,得到首次滤波图像和原图像估计直方图,根据该直方图确定模糊隶属度函数,然后对首次滤波图像中灰度小于25的像素点进行模糊加权均值滤波,该算法在不需要期望图像和高斯噪声方差的情况下能有效地去除噪声,同时能够很好地保护图像细节信息。     

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.     

Adaptive binarization method for degraded document images based on surface contrast variation

Document binarization is an important technique in document image analysis and recognition. Generally, binarization methods are ineffective for degraded images. Several binarization methods have been proposed; however, none of them are effective for historical and degraded document images. In this paper, a new binarization method is proposed for degraded document images. The proposed method based on the variance between pixel contrast, it consists of four stages: pre-processing, geometrical feature extraction, feature selection, and post-processing. The proposed method was evaluated based on several visual and statistical experiments. The experiments were conducted using five International Document Image Binarization Contest benchmark datasets specialized for binarization testing. The results compared with five adaptive binarization methods: Niblack, Sauvola thresholding, Sauvola compound algorithm, NICK, and Bataineh. The results show that the proposed method performs better than other methods in all binarization cases.     

基于边缘颜色对的车牌定位新方法

车牌定位是车牌自动识别系统中的一个关键问题．该文提出了一种新的基于边缘颜色对的车牌定位方法．首先进行彩色边缘检测,然后以每一边缘点为中心,垂直于边缘方向取一线形窗口,在窗口内检测边缘点两侧像素的颜色是否分别匹配车牌的底色与字符颜色,若是,则保留为候选车牌边缘点;然后进行形态滤波,剥离不符合车牌结构特征的区域,最后对候选车牌区域进行纹理特征的分析以确定真实车牌区域．该方法抓住了车牌背景与字符具有固定颜色搭配的重要特点,综合利用了车牌的结构特征和纹理特征,提高了车牌定位的可靠性．对各种条件下拍摄的163幅含有车牌的图像应用该算法,定位准确率达到98．2％。     

结合引导滤波的自适应多曝光图像融合

针对引导滤波产生的光晕、梯度反转现象，以及图像融合边缘细节丢失的现象，提出一种改进引导滤波的自适应多曝光图像融合算法。在引导滤波中根据梯度信息设定权重函数，并结合图像像素点和一定区域的均值创建函数，共同实现不同区域的纹理特性自适应；利用平均亮度与对比度、饱和度及曝光适中度的关系，设置权值函数，使加权平均融合过程中的权重值不再是固定的数值，而能够根据不同的图像亮度自适应调整，权重值也不同，使得融合后的图像质量更好；将原序列图的细节信息叠加到改进的引导滤波图像中，构建纹理细节层。实验结果削弱了光晕及梯度反转现象，使图像更加真实，细节更加清晰，并且对有小光源的图像处理效果更好。算法结果明显优于多曝光融合算法及引导滤波的多曝光图像融合，在信息熵、互信息和边缘信息评价中分别取得最高2.5%、30%和30%左右的质量提升。     

基于特征融合的粒子滤波在红外小目标跟踪中的应用

针对红外图像序列中的小目标跟踪问题,在分析红外小目标特点的基础上,提出了一种基于特征融合的粒子滤波目标跟踪算法。该方法利用粒子滤波支持目标特征融合的优点,提出将灰度特征和分形特征相融合,并将融合后的信息用于粒子权值的计算,从而大大提高了跟踪算法的稳健性。实验结果表明,和传统的粒子滤波算法相比,该算法能够更加准确、有效地跟踪红外序列中的小目标。     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

基于方向场分布率的静脉图像分割方法

提出了一种新的静脉图像分割方法, 该方法以方向场分布率(Distribution ratio of directional fields, DRDF)作为区分静脉纹路和背景的分割准则. 首先利用邻域信息和邻域分块模板计算图像中每个像素点的方向场以生成方向场图像, 然后根据方向场图像中像素点互补半圆区域内的方向场分布率和分布判定函数计算出8灰度级图像, 最后确定二值化参数将8灰度级图像二值化得到最终图像分割结果. 该方法结合静脉图像特征, 充分利用方向场图像的空间属性,克服了照度不均、粗细不均以及边界模糊等因素对分割造成的影响. 实验结果表明该方法对于静脉图像具有很好的分割效果.     

基于滤波器阵列与图割的彩色纹理分割

图割法因无法体现像素点的纹理区域特性而难以应用于纹理分割。针对该问题,提出一种基于滤波器阵列与图割的彩色纹理分割算法。利用构建的滤波器阵列提取图像的纹理特征,并加入图像的H、S、I分量值组成纹理-色彩特征向量,采用texton直方图作为彩色纹理的统计模型对纹理-色彩特征向量进行统计计算,通过直方图差计算像素点间的纹理相似度,再应用图割法中的规范割准则对彩色纹理进行分割。实验结果证明,该算法具有较高的分割准确性。