基于方向滤波器组的激光主动成像图像复原

动平台激光主动成像系统所成图像被模糊的同时受到多种噪声的污染,传统的复原方法对噪声敏感,难以取得令人满意的结果。为此,提出基于方向滤波器组的激光主动成像图像复原方法,使用不同的方向滤波器组对含噪模糊图像进行滤波,估计模糊核函数。对于每个方向滤波器,计算核函数在垂直滤波器方向的Radon变换。接着使用Radon逆变换得到二维模糊核函数的最终估计。根据模糊核函数,即可复原出无噪清晰图像。搭建激光主动成像系统进行实验,结果表明与近年来一些较好的模糊盲复原方法相比,该方法不仅拥有更好的主观效果,同时在客观评价指标(PSNR)上也有所提高。     

基于图像内容的中值滤波器设计及其FPGA实现

在图像处理领域里,中值滤波器经常用于去除图像的椒盐噪声。传统的中值滤波器存在图像模糊的缺陷。本文介绍了一种新的中值滤波器的结构,该滤波器首先检测图像是否存在噪声,然后决定是否进行中值滤波。因此该滤波器在去除图像噪声的同时,比传统的中值滤波器更好地保留了图像细节。     

一种视频监控图像条纹噪声去除算法

针对视频图像中图像传感器故障引起的等间距横纹噪声去除问题,提出了基于陷波滤波器的去除条纹噪声算法;首先将视频条纹图进行傅里叶变换,通过频域累积分布函数映射法构造自适应滤波器,用该滤波器对变换后的条纹图像进行滤波,最后用Laplacian算子进行锐化处理得到最终去噪声图像。实验结果表明,本算法适合用于去除有图像传感器故障引起的等间距横纹噪声。     

脉冲噪声图像的中-均值滤波算法

由于在图像信息的获取和传输过程中,图像常常受到不同程度的脉冲噪声污染。为了有效地去除高浓度脉冲噪声,提出了一种基于中－均值滤波器的噪声去除算法。该方法根据脉冲噪声特点,设定一个简单的噪声检测算子,根据噪声检测结果设定自适应滤波窗口,同时根据噪声密度选择中值和均值滤波器。为了更加有效地保留图像的原有信息,对非噪声点不做滤波处理。仿真结果表明,所提出的中－均值滤波方法不仅能有效地去除高浓度的脉冲噪声,而且能很好地保留图像的原有信息,并具有较短的滤波处理时间。     

基于离散余弦变换的水平集算法研究

针对传统水平集算法只对含有加性噪声的图像有较好处理结果的缺点,提出了一种基于离散余弦变换（DCT）的水平集研究算法。首先以图像中的每一点为中心构造隶属于图像的一系列子图像,对这些子图像进行二维DCT变换得到其变换系数,并受Lee滤波器可以去除乘性噪声的启发对这些系数进行运算,得到去除噪声后的平滑图像,再将平滑图像梯度的递减函数作为水平集演化方程的速度停止项,最后根据水平集演化方程对图像进行演化运算。实验结果表明,该方法能够在抑制乘性噪声的同时较好的对图像进行分割。     

基于TSPL的图像椒盐噪声去除新算法

在提出的三子集划分的灰度图像分层表示（TSPL）算法的基础上,主要研究对常见的椒盐噪声的去除。TSPL算法是一种新型的数字图像变换方法,其核心思想是用一系列具有解析形式的灰度函数逼近不规则的原图像灰度函数。在此分别处理由TSPL算法生成的各层基函数图像,将其划分为A,B,C三个区并将每个像素点赋值为0,1,2;结合图像椒盐噪声的特点,利用投票策略处理每个像素点的8?邻域,从而达到椒盐噪声去除的目的;最终通过TSPL算法对基函数重构来恢复原图像。采用在人类视觉系统的前提下提出的基于结构相似性的方法MSSIM算法作为图像质量评价的标准,实验结果表明,在主客观方面,该方法在去除噪声和保留图像细节方面明显优于传统的中值滤波方法。     

基于方向滤波器消除遥感图像孤立条带噪声的方法

条带噪声是影响遥感图像质量的重要因素之一.在分析遥感CCD图像孤立条带噪声产生原因和噪声模型的基础上,提出了基于二维方向滤波器抑制条带噪声的新方法.该方法利用了窄带方向滤波器良好的图像纹理方向的频率选择特性,将条带噪声与图像其它信息分离到不同的方向滤波器子带内,并在水平信息子带内采用均值补偿方法去除条带噪声.实验结果证明它不仅能有效去除CCD图像中的随机孤立条带噪声,还能保持原始图像的辐射入射幅度,保留细节信息,较传统的方法相比,性能有较大的提高.     

基于补偿特征图滤波算法去除光学相干层析造影成像俯视图的运动伪影

运动伪影是光学相干层析造影成像(OCTA)俯视图中的一个重要问题.提出一种补偿特征图像滤波(CEF)算法去除光学相干层析造影成像俯视图的运动伪影.首先,利用奇异值分解(SVD)将光学相干层析造影成像俯视图展开为一系列的特征图像;然后,利用高通特征图像和从第一特征图像中抽取得到的正交补偿积这两部分来重建恢复图像.实验结果表明,CEF算法能较好地去除条纹噪声,经CEF算法复原得到的图像有着较传统特征图滤波算法更好的图像质量.该算法为其他扫描成像系统去除类似的条纹噪声提供了新的参考.     

基于直方图的自适应图像去噪滤波器

对于那些明显偏离高斯型白噪声的加性噪声,如拖尾脉冲噪声,高斯脉冲噪声等,已有方法的滤噪性能会严重退化.为此,该文提出了一种去除脉冲噪声的新方法.该方法首先由被污染图像估计出原图像的直方图.然后应用模糊集理论,利用加权策略得到了一个符合图像灰度分布统计规律的模糊隶属度函数,以此隶属度函数构建一个加权平均滤波器. 新方法有效地利用了原图像的先验知识,能够根据图像区域特性差异及脉冲噪声强弱自适应地采用不同的滤波尺度.文章比较了传统滤波器、已有的模糊滤波器和本文方法的结果.实验表明本文方法具有更好的效果.     

EMCCD图像自适应模糊中值滤波算法研究

针对电子倍增CCD（EMCCD）图像噪声密度随着增益的变化而变化,提出了一种基于噪声点检测的自适应模糊中值滤波算法。该算法由模糊滤波模块和自适应模块两部分组成。首先,该算法对滤波窗口内的中心点进行噪声检测;然后对检测为噪声的像素点引入双阈值,并根据引入的阈值和滤波窗口内的中值建立噪声点的模糊隶属函数,根据模糊隶属函数对噪声点进行滤波处理后输出;最后采用自适应模块调整待处理图像的像素。仿真及实验结果表明,新算法不仅能够有效地将图像中的噪声去除,而且很好地保护了图像中的细节和边缘,PSNR比传统的自适应中值滤波算法平均提高了15 dB以上;该算法在低噪声密度情况下性能明显好于其他中值滤波器,在高噪声密度情况下性能也比较稳定。     

An efficient algorithm for the removal of impulse noise from corrupted images

In this letter, we propose an efficient algorithm, which can successfully remove impulse noise from corrupted images while preserving image details. It is efficient, and requires no previous training. The algorithm consists of two steps: impulse noise detection and impulse noise cancellation. Extensive experimental results show that the proposed approach significantly outperforms many other well-known techniques for image noise removal.     

A new impulse detector based on order statistics

The acquisition or transmission of digital images through sensors or communication channels is often interfered by impulse noise. It is very important to eliminate noise in the images before subsequent processing, such as image segmentation, object recognition, and edge detection. In this letter, a novel impulse noise-detection algorithm is presented, which can remove impulse noise from corrupted images successfully, and at the same time, without eliminating image details. The algorithm is based on the order statistics within a local window. Although our method is low in complexity when compared with some other complicated algorithms, experimental results show that it produces better restored images than many other existing techniques.     

多幅图像中值法在滤除噪声中的应用

数字图像在成像和传输过程中可能会夹杂一些噪声,如椒盐噪声和高斯噪声等。这些噪声可能会对图像处理结果产生消极影响,所以在图像处理前要对该图像进行平滑,因此提出了一种基于多幅图像中值的滤波方法。该算法首先对同一场景多次采集图像,然后将这些图像在相同位置上的灰度中值作为图像在该点的灰度值。为了验证算法的有效性进行了多次实验与传统算法比较,结果表明该算法不仅能最大程度地滤除椒盐噪声,并且对高斯噪声也有很好的抑制作用;同时,客观评价标准也证明,该算法明显优于传统方法。     

基于中值滤波和小波变换图像降噪的新算法

针对小波变换去噪方法仅适用于去除高斯白噪声,对于脉冲噪声得不到良好的降噪效果的问题,文中构造了一个新的小波阈值函数,并对中值滤波进行了改进,将此两者方法相结合进行图像去噪。仿真实验结果表明,新算法能够有效去除高斯白噪声和脉冲噪声,而且去噪后图像的峰值信噪比和均方误差都较单一算法得到改善,从而证明了该方法的有效性。     

一种基于中值-模糊技术的混合噪声滤波器

结合中值与模糊滤波技术,提出了一种新的图像混合噪声滤波算法。算法将受混合噪声污染的图像分为脉冲噪声点集与含有高斯噪声的像素点集两部分,首先进行灰度极值检测,进而借助邻域纹理信息准确检测出脉冲噪声,并以中值滤波滤除;对于含有高斯噪声的像素点则采用一种保护细节的模糊滤波器进行处理。实验结果说明算法不仅能有效地滤除脉冲与高斯混合噪声,而且可以较好地保护图像细节。     

A two-stage shearlet-based approach for the removal of random-valued impulse noise in images

In this paper, we introduce a novel two-stage denoising method for the removal of random-valued impulse noise (RVIN) in images. The first stage of our algorithm applies an impulse-noise detection routine that is a refinement of the HEIND algorithm and is very accurate in identifying the location of the noisy pixels. The second stage is an image inpainting routine that is designed to restore the missing information at those pixels that have been identified during the first stage. One of the novelties of our approach is that our inpainting routine takes advantage of the shearlet representation to efficiently recover the geometry of the original image. This method is particularly effective to eliminate jagged edges and other visual artifacts that frequently affect many RVIN denoising algorithms, especially at higher noise levels. We present extensive numerical demonstrations to show that our approach is very effective to remove random-valued impulse noise without any significant loss of fine-scale detail. Our algorithm compares very favourably against state-of-the-art methods in terms of both visual quality and quantitative measurements.     

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.     

一种新的图像去噪混合滤波方法

为了去除图像中混入的脉冲噪声和高斯噪声,提出了一种基于自适应中值滤波和模糊加权均值滤波的混合滤波方法.该方法首先进行噪声检测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模糊加权均值滤波算法,而对受脉冲噪声污染的像素则采用改进的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.     

Adaptive 3-D median filtering for restoration of an image sequence corrupted by impulse noise

An adaptive 3-D median filtering, which achieves optimal image quality as well as fast computing time, is proposed to remove the impulse noise from a highly corrupted image sequence. The proposed algorithm is compared with the widely used impulse noise removal algorithms with respect to the peak signal-to-noise ratio and the number of computations. The proposed algorithm preserves the image details which are not expected to be corrupted by impulse noise so that the number of computations can be minimized. It has good restoration performance whether the number of pixels corrupted by impulse noise is large or small. In the proposed algorithm, the impulse noise ratio, which is the ratio of the number of pixels corrupted by impulse noise to the total number of pixels, is estimated, and the restoration filtering is adaptively applied based on the estimated impulse noise ratio.