无人机视觉着陆中的图像去噪算法

针对无人机在视觉着落过程中,获取的图像受噪声影响,设计了一种改进的中值滤波器。首先将物理学中的引力概念引入到图像像素间;其次,给出了像素间的引力大小的数学表达式;最后通过像素间的引力来确定自适应窗口并进行相应的滤波处理。实验结果表明,与传统中值滤波以及开关中值滤波算法比较,该算法在图像去噪和细节保留方面有了很大的改善。     

基于双窗口和极值压缩的自适应中值滤波

为了提高中值滤波器的滤波性能和适应不同密度的椒盐脉冲噪声,从噪声检测的准确性、噪声滤除的有效性和滤波速度的实用性等3个方面分别对中值滤波方法加以改进,提出了一种基于双窗口和极值压缩的自适应中值滤波方法(DWECAMF)。该方法采用大窗口检测噪声和小窗口滤除噪声的滤波策略、压缩噪声滤除窗口内极大值和极小值策略以及自适应脉冲噪声滤除策略,以提高图像滤波性能,同时采用了移动滤波策略提高滤波速度以增强其实用性。实验表明,该方法在以上3个方面的性能都有极大提高,并且对不同密度的椒盐噪声都具有很好的滤波性能。     

改进椒盐去噪方法在井下图像中的应用

为了提高井下图像采集的质量,针对目前改进中值滤波算法的优缺点,提出了一种新的去除井下图像椒盐噪声的算法。该算法首先判断出图像中的噪声点和非噪声点,然后根据窗口内噪声点的密度大小自适应地确定滤波窗口的大小,并按照一定的规律赋予窗口内像素点不同的权重,最后采用加权中值方法处理图像中的噪声点。计算机模拟实验证明该方法不仅能有效地去除不同密度的椒盐噪声,而且能很好地保持图像的细节,滤波效果比已提出的中值滤波算法更好。     

椒盐噪声图像的非局部平均滤波算法

针对非局部平均(NLM)方法对椒盐噪声图像滤波效果较差的问题,通过引入噪声检测结果扩展NLM方法去除图像中椒盐噪声。在噪声检测阶段,利用图像的两个极值Lmin和Lmax把图像像素点分为非噪声点和噪声点。在滤波阶段,非噪声点的灰度值保持不变。对于噪声点,如果以该噪声点为中心的自适应滤波窗口内均为噪声点,则认为该噪声点位于图像自身灰度值为Lmin或Lmax的区域内,使用两个极值的统计结果进行恢复。否则,采用改进的NLM方法滤除噪声。构造联合噪声检测模板避免噪声点对相似权计算的干扰,噪声点的恢复值由非噪声点的灰度值加权平均得到。此外,采用迭代滤波策略对高密度噪声图像噪声点进行恢复。相关去噪实验结果证实了算法去噪的有效性,不足之处是算法的时间复杂度较高。     

局部特定空间关系统计特征的RVIN噪声检测器

目的 随机脉冲噪声（random-valued impulse noise，RVIN）检测器将局部图像统计值（local image statistics，LIS）作为图块中心像素点是否为噪声的判断依据，但LIS的描述能力较弱，在不同程度上制约了RVIN检测器的检测正确率，影响了后续开关型降噪模块的修复效果。为此，提出了一种基于局部特定空间关系统计特征的RVIN噪声检测器。方法 以局部中心像素点的8个邻域像素对数差值排序值（rank-ordered logarithmic difference，ROLD）并结合1个最小方向对数差值（minimum orientation logarithmic difference，MOLD）共9个反映局部特定空间关系的LIS统计值构成描述中心像素点是否为RVIN的噪声感知特征矢量，并通过在大量样本图块数据上提取的RVIN噪声感知特征矢量及其对应的噪声标签作为训练对（training pairs），训练获得一个基于多层感知网络（multi-layer perception，MLP）的RVIN噪声检测器。结果 对比实验从检测正确率和实际应用效果2个方面检验所提出的RVIN检测器的有效性，分别在10幅常用图像和50幅BSD （Berkeley segmentation data）纹理图像上进行测试，并与经典的脉冲噪声降噪算法中包含的噪声检测器以及MLPNNC （MLP neural network classifier）噪声检测器相比较，以漏检数、误检数和错检总数作为评价噪声检测正确率的指标。在常用图像集上本文所提RVIN检测器的漏检数和误检数较为平衡，在错检总数上排名处于所有对比算法中的前2名，为后续的降噪模块打下了很好的基础。在BSD纹理图像集上，将本文提出的RVIN检测器和GIRAF （generic iteratively reweighted annihilating filter）算法组合构成一种RVIN噪声降噪算法（proposed-GIRAF），proposed-GIRAF算法在50幅BSD图像上的峰值信噪比（peak signal-to-noise ratio，PSNR）均值在各个噪声比例下均取得了最优结果，与排名第2的对比算法相比，提升了0.471.96 dB。实验数据表明，所提出的RVIN噪声检测器的检测正确率优于现有的检测器，与修复算法联用后即可获得一种降噪效果更佳的开关型RVIN降噪算法。结论 本文提出的RVIN噪声检测器在各个噪声比例下具有鲁棒的预测准确性，配合GIRAF算法使用后，与经典的RVIN降噪算法相比，降噪效果最佳，具有很强的实用性。     

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

     

一种改进的自适应中值滤波算法

针对未知脉冲噪声强度的退化图像的去噪,提出了一种新的自适应中值滤波算法,该算法主要基于以下两点：（1）根据模糊数学里的模糊度理论及随机脉冲噪声本身的去噪特点,提出了模糊指标的概念,并通过反向二阶拟合来获得噪声的强度信息;（2）引入了反映图像边缘信息的Prewitt梯度算子,并通过实验来得到合适的梯度阚值,以更好地保持图像的边缘等细节信息.通过将该算法与传统的中值滤波、基于排序阈值的开关中值滤波以及Sorin Zoican提出的改进的中值滤波进行的对比实验表明,该算法对噪声的强度有很好的估计,不仅提高了噪声去除的自适应性,尤其对含噪声多的图像的处理效果更为理想.     

一种基于排序阈值的开关中值滤波方法

提出了一种基于排序阈值的开关中值滤波方法以克服图像滤波中去噪与细节保护的矛盾。该方法利用滤波窗口内像素点的排序信息，在极值中值滤波方法的基础上，将受脉冲噪声污染图像中的像素点进一步分为噪声点、边缘细节区和平坦区3种类型。通过对多种图像测试的统计结果，获得合适的分类器参数，然后利用类型判决，进行开关中值滤波，即对噪声点和平坦区进行中值滤波以得到良好的噪声滤除效果，而对边缘细节区不做处理以获得良好的细节保护效果。比较了标准中值滤波、极值中值滤波和本方法的结果。实验结果表明，本方法具有更好的效果。     

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。     

有效去除图像中脉冲噪声的新型滤波算法

提出一种基于局部极值噪声检测的迭代中值滤波算法．该算法集中了minrnax算法与PSM算法各自的优势，并将两种算法有机地结合起来．经过实验仿真并与其他滤波算法进行比较表明，该算法可以有效地去除图像中的脉冲噪声，尤其是在噪声密度非常大的情况下表现了很好的性能。     

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。     

Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic

A novel image filter based on type-2 fuzzy logic techniques is proposed for detail-preserving restoration of digital images corrupted by impulse noise. The performance of the proposed filter is evaluated for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise filters from the literature. Experimental results show that the proposed filter exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving thin lines, edges, texture, and other useful information within the image.      

脉冲噪声污染图像的迭代二值化分割

为使混有脉冲噪声的图像在去噪后取得更好的效果，对混有脉冲噪声的图像，先采用开关中值滤波消除噪声干扰，然后利用迭代算法实现图像的二值化分割，对提出的算法进行计算机仿真。结果表明：开关中值滤波能在有效去噪的同时很好地保护图像中的细节，对去噪后的图像再采用迭代算法进行二值分割可取得良好的效果。     

Context model based edge preservation filter for impulse noise removal

In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.     

Adaptive Impulsive Noise Removal in TV Picture Transmission

This paper presents the capability and real-time processing features of an adaptive filter for the removal of impulse noise in TV picture transmission. The basic method, which has been extensively tested on data corrupted by randomly distributed impulses, is first enhanced to deal with real TV pictures suffering from impulse noise. In particular, the characteristics of the real impulses are incorporated in the noise identification stage. Then the suitability of the method in terms of real-time processing for images corrupted by randomly distributed impulse noise and those corrupted by real impulse strokes is analytically and numerically investigated. Impulse corrupted TV picture sequences are used to demonstrate that the proposed method potentially provides a real-time solution to quality TV picture transmission.     

The solutions of equation-based noise detector for an adaptive median filter

Techniques of noise detection have been widely applied in impulse noise reduction. However, the phenomenon of pixel misclassification is very obvious in high noise density. In order to improve pixel identification, in this paper, the new noise detector is proposed. Based on solutions of equations, an estimated block of every 8×8 block of a noise image is generated. Then, according to relationships between these noise blocks and their estimated blocks, corrupted and uncorrupted pixels are identified. During image filtering, a noise-detection-based adaptive median algorithm is presented. Experimental results show that the proposed filter can well reduce the impulse noise and preserve more details of original images.     

邻域均值检测的迭代加权中值滤波算法

针对现有滤波算法在噪声检测与去除上存在的相应缺陷,提出了邻域均值检测的迭代加权中值滤波算法,对噪声检测与去除方法分别进行改进。算法根据噪声的灰度特征进行噪声检测,再基于邻域像素的相关性,用邻域的均值作进一步的检测;运用基于高斯曲面的加权算子,以迭代的方式,用邻域中信号像素的加权中值对噪声进行去除。实验结果证明,相对于现有滤波算法,所提算法具有更好的去噪性能,在保持高信噪比的同时,能很好地保持图像的纹理结构。     

一种基于模糊决策的开关矢量中值滤波方法

针对现有的彩色图像脉冲噪声去除方法没有区分滑动窗口中的像素是否为噪声像素而导致滤波效果差的问题, 提出一种基于模糊决策的开关矢量中值滤波方法。该方法首先利用开关条件判断像素是否被污染, 针对被污染的像素, 通过模糊数学理论构造适合脉冲噪声去除的隶属函数; 然后计算滑动窗口内所有像素的模糊隶属度, 并根据置信区间去除疑似噪声像素以优化滑动窗口的取值空间; 最后对优化后的滑动窗口应用矢量中值滤波(VMF)以去除噪声像素。与现有方法相比, 新的方法去除了滑动窗口中心像素的邻域疑似噪声, 从而有效提升了滤波效果。实验验证了该方法的高鲁棒性和实用性。     

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.     

SLAC: Statistical total lesion metabolic activity computation by fuzzy unsupervised learning of PET images

Accurate lesion metabolic response estimation is imperative for efficient tumor staging and follow-up studies. Positron emission tomography (PET) successfully images the lesion metabolic activity. Nonetheless, on course of accurate delineation, chances are high to end up with activity underestimation as, due to the limited resolution, the PET images suffer from partial volume effects. Recently, PET images were modeled as a fuzzy mixture to delineate lesions accurately. We extend this work by proposing a statistical lesion activity computation (SLAC) approach to robustly estimate the total lesion activity (TLA) directly from the modeled partial volume mixtures, without an explicit delineation. To evaluate the proposed method, PET scans of phantoms containing spherical and non-spherical lesions with increased activity uptake were simulated. The PET images were reconstructed with the standard clinically used maximum likelihood expectation maximization and an edge preserving maximum a posteriori (MAP) algorithm, both with resolution recovery. From these images, the TLA was estimated in each lesion using the proposed method and compared to the TLA estimation in the tumor delineations obtained with three state-of-the-art PET delineation schemes. SLAC outperformed TLA estimation via tumor delineation and showed robust against variation in reconstruction parameters. With reference to the ground truth knowledge, SLAC gives median $\delta $ TLA $~\approx $  5 % for spherical lesions. For more realistic non-spherical lesions, median $\delta $ TLA $~\approx $  15 %.