Wavelet domain non-linear filtering for MRI denoising

Feature-preserved denoising is of great interest in medical image processing. This article presents a wavelet-based bilateral filtering scheme for noise reduction in magnetic resonance images. Undecimated wavelet transform is employed to provide effective representation of the noisy coefficients. Bilateral filtering of the approximate coefficients improves the denoising efficiency and effectively preserves the edge features. Denoising is done in the square magnitude domain, where the noise tends to be signal independent and is additive. The proposed method has been adapted specifically to Rician noise. The visual and the diagnostic quality of the denoised image is well preserved. The quantitative and the qualitative measures used as the quality metrics demonstrate the ability of the proposed method for noise suppression.     

Switching non-local vector median filter

This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images.     

Audio-visual enhancement of speech in noise.

A key problem for telecommunication or human-machine communication systems concerns speech enhancement in noise. In this domain, a certain number of techniques exist, all of them based on an acoustic-only approach--that is, the processing of the audio corrupted signal using audio information (from the corrupted signal only or additive audio information). In this paper, an audio-visual approach to the problem is considered, since it has been demonstrated in several studies that viewing the speaker's face improves message intelligibility, especially in noisy environments. A speech enhancement prototype system that takes advantage of visual inputs is developed. A filtering process approach is proposed that uses enhancement filters estimated with the help of lip shape information. The estimation process is based on linear regression or simple neural networks using a training corpus. A set of experiments assessed by Gaussian classification and perceptual tests demonstrates that it is indeed possible to enhance simple stimuli (vowel-plosive-vowel sequences) embedded in white Gaussian noise.     

A recursive color image edge detection method using Green's function approach

In this paper, an extended version of image edge detector using Green's function approach is proposed for detection of edges in the color vector space field. In the proposed method, the relationship between the Red, Green and Blue components is considered to design a differential operator for detection of edges in color images. By using the proposed operator, partial derivatives of all components of color image can simultaneously affect on the edge detection process. Therefore the proposed method can preserve the vector nature of color images during the edge processing stages. Also, the proposed method is compared both quantitatively and qualitatively with other color edge detectors. Experimental results show that the proposed method can efficiently preserve the edges even when the color images corrupted with different levels of noise.     

Optimal processing of noisy images in a photodetector with limited dynamic range

A study aimed at optimizing noisy image processing under conditions of strong additive noise has been performed. An algorithm of optimal signal processing was developed and a possibility of improving image quality due to the subtraction of excess additive noise (which limits the photodetector dynamic range) was substantiated. The possibility of technical implementation of noise subtraction due to forced recombination of charge carriers in the photodetector is experimentally confirmed. The proposed approach to design processing systems makes it possible to improve the quality of recorded images under noisy conditions without any changes in the photodetector design.     

Laplacian based non-local means denoising of MR images with Rician noise

Magnetic Resonance (MR) image is often corrupted with a complex white Gaussian noise (Rician noise) which is signal dependent. Considering the special characteristics of Rician noise, we carry out nonlocal means denoising on squared magnitude images and compensate the introduced bias. In this paper, we propose an algorithm which not only preserves the edges and fine structures but also performs efficient denoising. For this purpose we have used a Laplacian of Gaussian (LoG) filter in conjunction with a nonlocal means filter (NLM). Further, to enhance the edges and to accelerate the filtering process, only a few similar patches have been preselected on the basis of closeness in edge and inverted mean values. Experiments have been conducted on both simulated and clinical data sets. The qualitative and quantitative measures demonstrate the efficacy of the proposed method.     

The study and application of a new filtering method on electronic speckle pattern interferometric fringe

It is important for the study of digital image processing to remove noises and enhance images because of various noises in images. The article is concerned with the recursive algorithm-filtering technique, which is capable of effectively removing noise and rapidly leading to a continuous gray-level distribution of a random image. By applying the method on speckle images of defects of rubber material, it is shown that this technique is the most effective and speedy method for the filtering of ESPI fringe patterns. Comparing the images before and after filtering, we can see the advantages of the novel technique clearly. It can increase the accuracy of measurement both in phase measurement and in quantitative evaluations of defects.     

Detection of binary images in noise

Detection of binary images in noise is considered in the light of statistical detection theory, with particular reference to information storage systems. The different disturbances are classified into system noise and transparency noise. The receiver operating characteristics (ROC) have been obtained when the scanned binary image is corrupted by additive white Gaussian noise and multiplicative speckle. It is shown that improvement in ROC with signal-to-noise ratio (SNR) is less pronounced in the presence of speckle.     

去除乘性噪音的主成分分析算法

雷达成像系统的进一步应用依赖于对图像中噪音的有效抑制.在目前现有消除噪音方法的基础上,基于图像的局部相似性,结合主成分分析法,提出一种新的有效去除乘性噪音的滤波算法.乘性噪音经对数变换后可转化为加性噪音处理.分析了对数域中噪音的类型.首先在图像的对数域,通过非局部方法选取局部相似块作为训练样本,利用主成分分析法提取出信号的主要特征.然后基于统计理论中最小均方误差估计法给出了一种适用于图像信息的阈值原则.最后分析了变换过程引起的偏差,由对数域的偏估计得到滤波图像.数值实验验证了新算法的有效性.对比于目前提出的变分方法,新算法处理后的图像有更高的信噪比和更好的视觉效果,且具有一定的实用性.     

一种保持非负整数值的图像加密算法

提出了一种能够使双随机相位图像加密方法的密文图像保持为非负整数值的变换——重构变换.重构变换包括预处理和频谱搬移两个过程,其主要特点为,在图像进行频域变换之前,通过叠加的方式将整数图像压缩成一半大小的复数图像,从而能缩小后续运算的计算空间;基于重构变换的双随机相位图像加密方法可以实现联合图像压缩和加密的效果.与基于混沌系统的数字图像加密方法相比,本文方法的密文图像具有更低的信息熵.实验结果表明,该方法具有较强的安全性,解密图像基本无失真,并且密文图像对加性噪音攻击具有一定的鲁棒性.     

A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image

Moiré pattern noise in Scanning Transmission X-ray Microscopy (STXM) imaging introduces significant errors in qualitative and quantitative image analysis. Due to the complex origin of the noise, it is difficult to avoid Moiré pattern noise during the image data acquisition stage. In this paper, we introduce a post-processing method for filtering Moiré pattern noise from STXM images. This method includes a semi-automatic detection of the spectral peaks in the Fourier amplitude spectrum by using a local median filter, and elimination of the spectral noise peaks using a Gaussian notch filter. The proposed median-Gaussian filtering framework shows good results for STXM images with the size of power of two, if such parameters as threshold, sizes of the median and Gaussian filters, and size of the low frequency window, have been properly selected.     

一种保持非负整数值的图像加密算法

提出了一种能够使双随机相位图像加密方法的密文图像保持为非负整数值的变换——重构变换.重构变换包括预处理和频谱搬移两个过程,其主要特点为,在图像进行频域变换之前,通过叠加的方式将整数图像压缩成一半大小的复数图像,从而能缩小后续运算的计算空间;基于重构变换的双随机相位图像加密方法可以实现联合图像压缩和加密的效果.与基于混沌系统的数字图像加密方法相比,本文方法的密文图像具有更低的信息熵.实验结果表明,该方法具有较强的安全性,解密图像基本无失真,并且密文图像对加性噪音攻击具有一定的鲁棒性.     

基于多引导滤波的图像增强算法

图像增强技术可以有效地突出图像中的有用信息,已广泛应用于多个领域.现有的图像增强算法往往无法应对自然图像中复杂的梯度分布,难以准确保持图像中前景与背景的边缘信息.为了改善输出图像的边界过平滑问题,本文提出了一个基于多引导滤波的图像增强算法.首先,设计了一个以滤波核为变量的通用图像优化模型,现有的联合滤波器可视为该模型的解;然后,依据集成学习的思想,将联合滤波器中的单幅引导图像扩展到多幅,以更好地利用引导图中的结构信息进而获得更好的输出结果,并给出了一个多幅引导图的来源途径;最后,对多幅输出图像进行平滑,在图像优化模型中加入正则化项,以确保由多引导滤波得到的不同滤波输出保持一致.实验结果表明,本文算法在抑制图像噪声的同时,可以更好地保留物体的边界信息,从而使图像的信噪比进一步提升.     

Distributions of singular values for some random matrices

The singular value decomposition is a matrix decomposition technique widely used in the analysis of multivariate data, such as complex space-time images obtained in both physical and biological systems. In this paper, we examine the distribution of singular values of low-rank matrices corrupted by additive noise. Past studies have been limited to uniform uncorrelated noise. Using diagrammatic and saddle point integration techniques, we extend these results to heterogeneous and correlated noise sources. We also provide perturbative estimates of error bars on the reconstructed low-rank matrix obtained by truncating a singular value decomposition.     

激光相干场成像散斑噪声复合去噪方法

噪声是影响激光相干场高分辨成像系统像质的重要因素,激光相干场成像系统既受背景光加性噪声影响,又受激光乘性散斑噪声影响.为解决激光相干场成像系统受激光乘性散斑噪声和背景光加性噪声叠加引起的成像像质退化效应问题,从噪声抑制角度提高激光相干场系统高分辨成像像质,研究建立了激光散斑乘性噪声和背景光加性噪声对大气下行链路激光回波场信号影响干扰模型,并基于该模型提出了一种基于同态滤波和稀疏基追踪级联复合去噪算法.首先基于同态滤波理论将激光乘性散斑噪声转化为加性噪声,再由高通滤波器滤除散斑噪声,最后采用基追踪稀疏理论方法抑制背景光等加性噪声对像质的影响.研究表明,较现有单一去噪方法,该级联复合去噪方法可一次性消除激光乘性散斑噪声和背景加性噪声两种不同性质的噪声,有效改善了激光相干场成像质量.     

Phase-only filtering based on sliced orthogonal nonlinear generalized decomposition

The sliced orthogonal nonlinear generalized (SONG) decomposition and correlation have been demonstrated to be powerful tools in digital image processing and promising for nonlinear optical information processing. In this paper, we propose an optical phase-only filtering system based on SONG decomposition (PBS), in which the phase-only filtering of the target and the input scene binary slices are performed separately by pairs and then added together. We numerically show that the PBS has extremely high and sharp output correlation peak compared with other optical correlators. Furthermore, such a SONG decomposition based phase-only filtering naturally inherits the nature of SONG decomposition, which has strong robustness to additive Gaussian noise and substitutive noise, and also the high light efficiency of phase-only filtering. We demonstrate that the PBS may serve as an optimized optical correlation scheme, which is promising in nonlinear optical pattern recognition.     

基于双树复小波与波原子的图像扩散滤波

图像的非线性扩散滤波来源于热方程的思想,其关键在于计算适当的扩散系数和控制扩散方向. 在已有的扩散模型中,由于扩散系数仅依赖于图像的梯度,因而这类模型容易受噪声的干扰;同时,图像的细节信息(如纹理)容易被误认为是噪声而被去除. 为克服这些不足,首先给出了一种采用双树复小波变换计算扩散系数的方法;然后设计了一种用于图像滤波的非线性扩散模型,最后提出了基于双树复小波变换和波原子阈值相结合的图像滤波算法. 仿真结果表明,所提出的算法在对含噪图像滤波的同时,能够较好地保持图像的边缘和纹理等细节信息. 关键词： 图像扩散滤波 非线性扩散 波原子 双树复小波变换     

Random noise filtering method based on the inter-frame registration

One of the problems to be solved in image processing is how to eliminate image noise effectively. In this work, we brought forward a random noise filtering method based on the inter-frame registration. Firstly, we calculated the relative displacement of the adjacent frames by a registration algorithm. Then we divided the image into the overlapping area and the non-overlapping area according to the relative displacement. Finally, we do noise reduction processing for these two areas respectively. The experiments results indicate that the proposed method can reduce noise in both spatial and time domain of video images. The main advantage is that it cannot only remove noise, but also effectively protect the image edge and detail information. Besides, it not only maintains the de-noising effect of traditional inter-frame algorithm, but also is suitable for moving targets. It has better real-time performance and wider application range.     

Novel infrared image enhancement technology based on the frequency compensation approach

A novel infrared image enhancement method has been proposed in this paper. Our aim is to develop a detail enhancement method which is focused on the frequency feature of the image. The proposed method is following the most popular strategy of enhancing the infrared images nowadays, but concentrating on the frequency domain. Firstly, the original image is separated by a guided image filter into detail layer and the base layer. Quite unlike the traditional methods, we use the guided image filter to eliminate most of the noise and weak signal of the scenario. Then, by a designed iteration process, the higher frequency of the scenario will be calculated back and add to the detail layer. The noise will not be enhanced because the iteration is only focused on the leftover scenario frequency. We run many tests on the raw data captured by the 320 × 256 HgCdTe cooled thermal imager, and make a comparison between our approach with the previous method of bilateral filtering digital detail enhancement and guided image filtering digital detail enhancement. Figures and analytical data show that our method is better than the previous proposed researches. Our method could effectively process the infrared image with less noise and artifacts, which has potential applications in testing, manufacturing, chemical imaging, night vision, and surveillance security.     

Nonlinear transformations and logarithmic filtering in coherent optical systems

While photographic film allows only a limited class of nonlinear transformations to be applied to pictures, a much wider class can be realized by use of halftone contact screens. A contact screen which performs a logarithmic transformation has been studied both theoretically and experimentally. The screen performs properly over two log units of exposure. Multiplicatively formed images have been subjected to logarithmic filtering with good results. Possible applications include removal of speckle, extraction of images from multiplicative noise, and biomedical image processing.