小波多分辨分析的频带阈值去噪方法

提出了一种基于小波多分辨分析的频带阈值去噪方法,并给出了此方法的主要算法步骤。用此方法与单一阈值方法分别对一个实例信号进行了去噪处理,并基于两个客观评价标准：信噪比（SNR）、均方根误差（RSME）,评估了去噪效果。实验结果证明了提出的方法是一种更有效的语音去噪方法,大大提高了信噪比,改善了清晰度。     

基于波原子阈值算法的OCT图像降噪技术

为了消除或缓解光学相干断层成像方法中散斑等噪声对OCT图像像质退化的影响,提出了基于波原子阈值去噪算法。波原子变换是一种新型的二维多尺度变换,且满足曲线波的抛物比例尺度关系和各向异性征;波原子适用于模式的任意局部方向,能够对轴方向的各向异性模式稀疏展开。本文利用波原子阈值去噪算法,对人眼眼底组织和手指指尖皮肤的OCT图像进行降噪处理,并与传统的小波阈值算法和快速曲波算法对OCT样品图像去噪效果进行对比分析。结果表明,基于波原子阈值去噪方法能够有效地抑制OCT图像散斑噪声,并能保持图像边缘细节特征。     

Bandelet变换在金属断口图像去噪中的应用

Bandelet变换是一种基于边缘的图像表示方法,能自适应地跟踪图像的几何正则方向。本文中首先用小波去噪方法对带噪声的金属断口图像进行预处理,利用软阀值函数获得阀值T,进而获取去噪后图像的四叉树结构,并沿Bandelet块的最佳几何方向进行曲波变换,最后利用SUREShink 计算各Bandelet块的自适应阈值,然后采用多层软阈值去除噪声,进行Bandelet逆变换重构图像。实验表明同传统的小波子带多阈值去噪法相比, 该算法不仅提高了去噪后图像的峰值信噪比(PSNR),而且具有更强的边缘保持能力。     

基于脊波变换的扩频水印算法

针对脊波变换域比小波变换域更适合表示图像线状边缘的奇异性的特征,本文设计出一种新的图像水印算法,即将图像分块脊波变换,在脊波变化域上的高频系数中加性地嵌入经零均值互不相关伪随机序列扩频调制后的水印。实验结果表明,本文算法不仅可以抵抗如压缩、加噪、滤波和剪切等各种常见攻击,还能抵抗图像灰度增强和减弱攻击,与小波域水印算法的比较也说明了本文算法的优势。     

基于复轮廓波变换的图像消噪

为了克服实轮廓波图像消噪后广泛存在的混叠现象,研究了基于双树复小波级联方向滤波器架构的复轮廓波变换图像消噪的若干性质,证明了对于高斯白噪声图像,该变换具有更好的分割能力和抑制能力,并在此基础上提出了一种基于该变换的图像消噪算法.该算法采用蒙特卡罗方法来确定门限收敛因子,并采用这些因子修正3σ准则,对变换域系数模值采用硬阈值处理.图像消噪实验结果表明:该消噪算法比基于实轮廓波变换的消噪算法,具有更高的峰值信噪比和更好的视觉效果.     

基于线性混合小波基的图像去噪

单小波基由于时频特性难以与复杂的图像特征相匹配,限制了小波闽值算法在图像去噪效果上的进一步提高.提出了一种基于线性混合小波基的图像去噪方法,将多个不同特性的正交小波基进行线性混合构成一个新的小波基,用该混合小波基对图像进行分解后再通过阈值处理实现去噪.调节混合系数,可使混合小波基的时频特性与图像特征相匹配,从而提高小波阈值去噪效果.实验结果表明,该方法去噪效果优于参与混合的各单小波基去噪效果,其峰值信噪比(PSNR)最大可提高3.5 dB.     

基于脊波变换与混沌系统的图像加密技术

提出了一种基于脊波变换以及混沌序列的快速图像加密算法．利用脊波变换的基本原理,对秘密图像以及由混沌序列生成的广义混沌图像进行脊波变换,然后把脊波变换后系数进行融合,最后利用脊波逆变换进行图像重构得到结果图像．数值试验表明该算法简单易行,抗攻击能力较强.     

基于波原子变换的三维地震信号盲去噪

基于波原子变换的三维地震信号盲去噪算法,利用基于块的噪声估计算法估计信号噪声,采用循环平移处理信号并进行波原子变换,利用估计的噪声标准差按不同尺度分层设置阈值并进行修正,再采用改进的阈值函数处理波原子变换系数,进行波原子反变换与逆循环平移,得到去噪后三维地震信号。对含噪的合成与实际地震信号去噪,并与小波、双树复小波、曲波及传统波原子变换的去噪结果对比;结果表明,该算法较其它对比算法有明显优势,且随含噪量的增加,去噪优势愈加明显。从输出信噪比、均方误差以及峰值信噪比等评价指标可知,基于波原子变换的三维地震信号盲去噪算法去噪效果最佳,其次为传统波原子变换算法,然后为曲波变换与双树复小波变换算法,传统小波变换算法的去噪效果最差。     

基于改进暗通道算法的图像去雾研究

为了提高图像去雾效果,提出一种改进暗通道(IDCP)算法,通过缩小图像结合3个暗通道线性拟合方法对透射率进行精确计算,在大气值一定区间内利用四叉树细分算法求取图像的大气光值最终估计值,为避免图像饱和度偏低进行了颜色补偿。实验仿真显示该算法去雾后的图像在视觉上更加清新自然,对不同的图像的客观评价值优于其他算法。     

基于非局部均值算法的地震随机噪声滤波

为了改善地震图像的质量以利于勘探,提出了一种基于非局部均值滤波抑制地震图像随机噪声的新算法。非局部算法最初是一种用于图像去噪法。该算法对图像的每一个像素点（或数据）去噪只需考虑像素点的相似性,而无需考虑像素点空间上的距离。非局部均值所对数据没有假设前提,除了数据结构具有一阶冗余度。由于这个假设对大部分地震数据是成立的,所以我们提出了对地震数据随机噪声去噪的非局部算法。合成地震记录和实际数据使用非局部去噪算法,与传统算法（如：中值滤波,高斯滤波）相比,既对随机噪声进行了抑制,又不会降低地震同相轴陡变处或同相轴弯曲处的分辨率,提高了图像的质量。     

More than Fifty Shades of Grey: Quantitative Characterization of Defects and Interpretation Using SNR and CNR

The quantitative characterization of defects in images is commonly performed using the signal-to-noise ratio (SNR). However, there is a strong debate about this measure. First, because there is no single accepted definition of SNR. Second, because the SNR measurements are highly affected by the regions used to estimate the power of the signal and noise in the image. This work provides an overview of some of the most commonly used SNR measures. Images with different sources of noise, and defects with different contrasts, are used to evaluate and compare the ability of these measures to quantitatively characterize defects. The measures are also evaluated when the images are transformed using common image processing operations, including filtering and gamma correction. This work also proposes a methodology to define the regions used to estimate the power of the signal and noise in the images. Two alternative procedures are proposed weather prior information is available about the inspected specimen or not. The proposed methodology is applied on real data from infrared testing, where the considered SNR measures are evaluated.     

红外图像中的自适应维纳滤波噪声抑制技术

红外图像存在信噪比低、对比度差的问题,在目标辐射强度低、距离远以及恶劣天气状况下尤为严重.为了提高图像信噪比,提出一种红外图像中的自适应维纳滤波噪声抑制算法.算法突破了维纳滤波要求未退化图像和噪声功率谱已知的技术瓶颈,基于Canny边缘检测算子构建了平滑区域噪声方差的邻域估计准则,实现了红外图像噪声的自适应抑制.实验表...     

Two-dimensional noise-robust blind deconvolution of ultrasound images

This paper presents a new method for 2-D blind homomorphic deconvolution of medical B-scan ultrasound images. The method is based on noise-robust 2-D phase unwrapping and a noise-robust procedure to estimate the pulse in the complex cepstrum domain. Ordinary Wiener filtering is used in the subsequent deconvolution. The resulting images became much sharper with better defined tissue structures compared with the ordinary images. The deconvolved images had a resolution gain of the order of 3 to 7, and the signal-to-noise ratio (SNR) doubled for many of the images used in our experiments. The method gave stable results with respect to noise and gray levels through several image sequences     

基于高斯滤波的扫描图像去网

运用高斯滤波基本理论,通过对RGB图像进行分通道处理,即对R,G,B等3个通道分别进行高斯滤波处理,再经通道合并为彩色图像,并对经过处理后的彩色图像进行锐化处理。用本去网算法与扫描仪自带的去网算法进行了比较,在主观的基础上结合清晰度值,提出的去网算法所达到的效果更好。     

Noise Reduction Method for Ultrasonic TOFD Image Based on Image Registration

Influenced by random noises from inhomogeneous material scattering and fluctuation of detected electric signals, the signal-to-noise ratio (SNR) of ultrasonic time-of-flight-diffraction (TOFD) image decreases significantly. For the noise reduction of TOFD images, several D-scanned TOFD images with different distribution of noise characteristics are obtained through repeating detection and slightly and randomly changing the probe’s initial position each time. The registered images then are processed by shift-and-add (SAA) technique to reduce the noise level of the TOFD images. Besides, correlation image registration algorithm based on optimization method was established to avoid the shift of TOFD images due to slight change of probe’s initial position. Noises in the registered images show stochastic behavior at the same position. In order to verify reliability of the algorithm, an experimental TOFD detection system for weld defects has been designed to acquire and experiment with TOFD images. The experiment results have been evaluated in terms of cross correlation coefficient, SNR and standard variance of images. The results show that the proposed method could effectively enhance SNR of TOFD images and improve the ability to identify weld defects of materials.     

Filter for biomedical imaging and image processing

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a priori, knowledge about the type of noise corrupting the image. This makes the standard filters application specific. Widely used filters such as average, Gaussian, and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high-frequency details, making the image nonsmooth. An integrated general approach to design a finite impulse response filter based on Hebbian learning is proposed for optimal image filtering. This algorithm exploits the interpixel correlation by updating the filter coefficients using Hebbian learning. The algorithm is made iterative for achieving efficient learning from the neighborhood pixels. This algorithm performs optimal smoothing of the noisy image by preserving high-frequency as well as low-frequency features. Evaluation results show that the proposed finite impulse response filter is robust under various noise distributions such as Gaussian noise, salt-and-pepper noise, and speckle noise. Furthermore, the proposed approach does not require any a priori knowledge about the type of noise. The number of unknown parameters is few, and most of these parameters are adaptively obtained from the processed image. The proposed filter is successfully applied for image reconstruction in a positron emission tomography imaging modality. The images reconstructed by the proposed algorithm are found to be superior in quality compared with those reconstructed by existing PET image reconstruction methodologies.     

Hybrid retinal image registration using phase correlation

Abstract

This paper deals with registration of retinal images, which were taken by high-resolution digital colour fundus cameras. The proposed method describes successful application of phase correlation method. It combines several basic steps — global correction of shift, rotation and scaling, detection of landmarks, their correspondences and finally image registration using second-order polynomial model. The method is tested on two sets of images. The first set contains images from the diabetic patients where many retinal pathologies can disturb the registration process. The second set contains images from healthy subjects, which were acquired by different illumination conditions. The method was evaluated using four different criteria - tree objective and one subjective. These criteria are also compared. The achieved registration accuracy of the landmarks position error is 1·13 and 0·93 pixels for respective image sets. Finally, the simple scheme for retinal pathology visualisation of registered fundus pairs is presented.     

Neuro-wavelet based intelligent medical image fusion

Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION-DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state-of-the-art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases.     

Microarray image enhancement by denoising using stationary wavelet transform

Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.     

一种图像增强空域客观评价方法

针对图像增强效果常用主观评价而没法定量客观评价,提出一种空域结合BRISQUE和JND的图像增强客观评价方法。该方法空域内将测试图像分别进行BRISQUE失真评分和JND视觉评分,然后将所得分数以0.5的权重进行加权处理,所得总分即为增强后图像的客观评价得分。为验证所提方法的有效性,进行了系列实验:首先,认证图像失真相同背景亮度增大BRISQUE值不变,而视觉JND值随之改变;其次,认证相同背景亮度不同失真图像的JND值不变,而BRISQUE值不同;最后对增强后的图像应用所提算法进行评分,得到Score最高分为0.790 5,与主观评价结果一致,而PSNR、SSIM的评分最高为∞和1,但都是和原图像本身比较,不能表明图像增强效果。从而证明所提算法能够定量地对图像增强进行客观评价。