一种基于图像信息熵的自适应滤波算法

提出一种基于图像最小信息熵的自适应滤波算法,利用局部信息熵最小的办法估计图像中的噪声感染程度,从而自适应地确定修正后的Alpha均值滤波器中滤波窗口的大小和去掉灰度最大和最小的像素个数,然后进行滤波。实验结果表明此算法较现有的中值滤波和均值滤波器的滤波能力有显著提高,而且在图像的局部特征和细节保持方面优于现有的滤波算法,并克服了修正后的Alpha均值滤波器非自适应性的弱点。     

基于ROLD统计量的混合噪音线性滤波算法

提出一种基于ROLD统计量的混合噪音线性滤波算法（RLMF）。算法把用来检测脉冲噪音的ROLD统计量运用于混合噪音的滤波算法上,提高了混合噪音中脉冲噪音成分的检测效率,它不仅适用于恢复被混合噪音污染的数字图像,而且也适用于恢复被纯脉冲噪音或纯高斯噪音污染的数字图像。仿真实验证明,RLMF滤波后的图像视觉效果和PSNR均优于已知的同类滤波器。     

类型2模糊系统模型组合的噪声滤波器

通过对区间类型2模糊系统（interval Type-2 FLS）和高斯类型2模糊系统（Gaussian Type-2 FLS）进行组合,提出了适合图像脉冲噪声滤波的TFLS滤波器。用噪声密度不同的数字图像测试TFLS滤波器的性能,同时与区间类型2模糊系统FLS滤波器等进行对比。实验结果表明：TFLS滤波器在去除图像的脉冲噪声的同时,能较好地保留图像中的边缘细节信息,较显著改善图像视觉质量,客观评价指标PSNR值也有较明显提高。     

图像去噪混合滤波方法

传统均值滤波和中值滤波对高斯型噪声和椒盐型噪声有着不同的滤波特性。实际滤波时，由于图像往往会受到两种不同性质噪声的同时干扰，因此，单独采用中值滤波或均值滤波都不会达到最好的去噪效果，为了能同时对两种不同性质的噪声进行滤除，现提出了一种新的混合滤波算法，该算法首先利用局部阈值把受高斯型噪声污染的像素和受脉冲型噪声污染的像素区别开来，然后对受高斯噪声污染的像素采用均值滤波算法，而对受椒盐噪声污染的像素则采用中值滤波算法进行去噪。仿真结果证明，该方法更具有实用性和有效性。     

一种针对彩色图像随机值脉冲噪声的矢量滤波方法

为了去除彩色图像随机值脉冲噪声,提出了一种新的矢量滤波方法。该方法对图像的平滑区域和边缘区域的滤波工作分开进行,平滑区域滤波方法将窗口分成多个区域,然后基于矢量中值和平滑区域像素的特征检测出平滑区域的信号,边缘区域的滤波是在已知信号的基础上对非信号进行矢量中值滤波。仿真实验结果表明,该方法能够有效地去除彩色图像的随机值脉冲噪声,尤其当噪声密度较高时,去噪效果明显优于传统的矢量中值滤波。     

Data mining based noise diagnosis and fuzzy filter design for image processing

In image processing, both diagnosis of noise types and filter design are critical. Conventional filtering techniques for image restoration such as median filter and mean filter are not effective in many cases, such as the case lacking the information of noise types or the case having mixed noise in images. This paper develops a data mining approach for noise type diagnosis, and proposes a fuzzy filter design for enhancing the quality of noise corrupted images. The experimental results demonstrate that the proposed technique outperforms the conventional filters, particularly for dealing with the images corrupted by mixed noise with additive Gaussian noise and impulse noise.     

一种改进的脉冲噪声滤波方法

由于数字图像在生成与传输过程中容易受到脉冲噪声的污染,往往造成后续处理难以为继。为了改善图像质量,需要对图像进行去噪处理。针对传统中值滤波及其它非线性滤波方法在去除图像脉冲噪声时存在的不足,本文提出了一种改进的去噪方法:在滤波之前进行一次脉冲噪声检测,确定受到噪声污染的像素点,并进行记录标识;然后根据检测结果进行改进的中值滤波:只对判断为噪声点的像素进行处理,不仅考虑了标准中值,也分情况利用了中值的前一个值和中值的后一个值的信息。实验表明,改进方法不仅在滤除脉冲噪声方面相比其他非线性滤波有很大改进,而且它可以更好地保护图像的细节特性,对图像的后续处理有很好的价值。     

模糊加权均值滤波器

主要研究了受混合噪声污染图象的降噪滤波问题,运用模糊数学思想提出了一种基于模糊隶属度的加权均值滤波器。该算法利用模糊隶属度函数的概念,对均值滤波器的权值加以优化,使其不仅在降低高斯噪声的能力方面较均值滤波有所提高,而且对于脉冲噪声及混合噪声也有很好的抑制能力。     

一种新的基于PCNN的图像脉冲噪声滤波算法

为了更好地滤除图像中的脉冲噪声,在分析脉冲耦合神经网络(PCNN)的工作机理的基础上,在PCNN中引入了点火映射图的概念,并提出了一种新的基于PCNN点火映射图的图像脉冲噪声滤波器。该方法首先通过运行PCNN把噪声图像转化为点火映射图,然后利用点火映射图对图像中的脉冲噪声进行定位,最后仅对定位的噪声进行自适应滤波。计算机仿真实验表明,该方法的去噪效果优于传统方法,从而验证了该方法的有效性、合理性。     

Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images

This paper proposes a new median filter using prior information to capture natural pixels for restoration. In addition to being very efficient in logic execution, the proposed filter restores corrupted images with 1–99% levels of salt-and-pepper impulse noise to satisfactory ones. Without any iteration for noise detection, it intuitively and simply recognizes impulse noises, while keeping the others intact as nonnoises. Depending on different noise ratios at an image, two different sets of masked pixels are employed separately for the adoption of candidates for median finding. Furthermore, no limit to the size of mask windows assures that a proper median can be found. The simple logic of the proposed algorithm achieves significant milestones on the fidelity of a restored image. Moreover, the very fast execution speed of the proposed filter is very suitable for being applied to real-time processing. Relevant experimental results on subjective visualization and objective digital measure are reported to validate the robustness of the proposed filter.     

Salt and pepper noise filtering with fuzzy-cellular automata

A new image denoising algorithm is proposed to restore digital images corrupted by impulse noise. It is based on two dimensional cellular automata (CA) with the help of fuzzy logic theory. The algorithm describes a local fuzzy transition rule which gives a membership value to the corrupted pixel neighborhood and assigns next state value as a central pixel value. The proposed method removes the noise effectively even at noise level as high as 90%. Extensive simulations show that the proposed algorithm provides better performance than many of the existing filters in terms of noise suppression and detail preservation. Also, qualitative and quantitative measures of the image produce better results on different images compared with the other algorithms.     

Adaptive two-pass median filter based on support vector machines for image restoration

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.     

A novel decision-based adaptive feedback median filter for high density impulse noise suppression

The qualitative performances of the digital image processing methods are degraded due to the presence of impulse noise. The conventional median filter and its advanced versions somehow manage to remove the noise from image but cannot preserve the image details. In this paper, a novel decision based adaptive feedback median filter is proposed to suppress the high density noise and preserve the details of the image. The proposed method detects the corrupted or noisy pixels by analyzing the neighbours in a decisive manner, which is a challenging task for the different types of images and noise. It predicts a local threshold by analyzing the neighbours to decide the adaptive nature of the feedback median filter. The feedback mechanism is adapted to enhance the qualitative results. Various types of images and noise densities have been used to evaluate the performance of the proposed method. The qualitative and quantitative performances have been measured in terms of Peak Signal-to-Noise Ratio, Image Enhancement Factor and Structural Similarity Index. The experimental results show that the qualitative and quantitative performances are superior over existing methods and the computational time is comparable as well.

     

An efficient approach for the removal of impulse noise from the corrupted image using neural network based impulse detector

A new efficient approach to detect the impulse noise from the corrupted image using feed forward neural network (FFNN) is presented. A modified version of the arithmetic mean filter is proposed to remove the detected impulse noise. The performance of proposed noise detection approach is analyzed using the performance measures such as False Alarm Ratio (FAR), Missed Noise (MN) pixels and Falsely Detected Noise (FDN) pixels. The simulation results show that these performances are robust even at higher percentage of noise. The filtered result is compared with the other recent approaches in terms of Peak Signal to Noise Ratio (PSNR). The proposed method produces remarkably good results both in quantitative measures and qualitative judgments of image quality.     

Fuzzy based Impulse Noise Reduction Method

In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as detail preserving capability. Based on the criteria of peak-signal-to-noise-ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure we have found experimentally that the proposed method provides much better performance than the state-of-the-art filters. To analyze the detail preservation capability of the proposed filter sensitivity analysis is performed by changing the detail preservation module to see its effects on the details (texture and edge information) of resultant image. This sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.     

基于噪声检测的总变分去噪算法

对受高斯和脉冲混合噪声污染的数字图像去噪方法进行了研究,提出了一种基于噪声检测的自适应总变分(TV)去噪算法。提出的改进算法采用两步迭代框架实现:脉冲噪点检测和全变分图像恢复。第一步中,考虑到脉冲噪声污染的像素点不包含原图像有效信息,采用一种局部统计值,即邻域像素间的随机绝对差排序值(ROAD)估计出噪点的位置;第二步中,采用L2-TV方法进行去噪处理,并对上述过程进行迭代处理,得到去噪图像。在噪点估计过程中引入脉冲噪点水平参数,这样处理的优势在于可更准确地检测出脉冲噪点;而L2-TV去噪方法可很好地去除高斯噪声,两者结合有效地解决了TV算法存在误判图像脉冲噪声为边缘而产生假边缘的问题。与现有典型去噪方法的比较实验表明,该迭代去噪算法,即TV-ROAD算法,既能够去除混合噪声,又可以保留图像细节特征。     

一种基于噪声拓扑结构的滤波算法

基于脉冲噪声的特点,提出了一种新的非常有效的脉冲噪声的滤波算法。该算法将滤波过程分为两步进行,即第1步对图像的脉冲噪声点进行标识,第2步再对标识的噪声点进行滤波。在第1步这中,充分考虑到脉冲噪声的特点,主要利用了噪声的拓扑连通性;在第2步时,利用噪声点周围非噪声点的信息,来对噪声点进行修复。最后进行了仿真试验,并与传统中值滤波算法和开关中值滤波算法进行了比较,试验表明,在信噪比和细节保留方面,该算法要明显优于它们,特别是对高强度的脉冲噪声也有比较好的效果。     

一种基于多尺度噪声检测的图像中值滤波器

介绍了标准中值滤波与有效中值滤波的概念,提出了一种基于自适应多尺度噪声检测的中值滤波器,可用于恢复被椒盐脉冲噪声污染了的图像。滤波器将输入图像像素分为有效信号类、脉冲噪声类和恒定区域类,对各类像素采用不同的方法进行滤波处理。实验结果证明,本文算法的性能比现存的其它许多算法有了显著的提高,而且便于实现。     

基于模糊神经网络的脉冲噪声滤波器

针对一般模糊神经网络结构复杂、不利于硬件实现的问题,提出了一种基于Sugeno型模糊神经网络的新型脉冲噪声滤波器,该滤波器采用神经网络的结构设计,有利于噪声模式的检测,其内含于神经网络中的模糊推理机制不仅能够有效地滤除脉冲噪声,而且又不破坏图象的细节,该滤波器还采用能够获得全局解的遗传算法来对网络进行调整,初步研究表明,该模糊神经滤波器在滤除景物图象中的脉冲噪声方面,优于标准中值滤波器。     

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。