Image restoration by adaptive-neighborhood noise subtraction

A new algorithm for image restoration in the presence of additive white Gaussian noise is presented. This algorithm is based on a new, adaptive method to estimate the additive noise. The basic idea in this technique is to identify uniform structures or objects in the image by use of an adaptive neighborhood and to estimate the noise and the signal content in these areas separately. The noise is then subtracted selectively from the seed pixel of the adaptive neighborhood, and the process is repeated at every pixel in the image. The algorithm is compared with the adaptive two-dimensional least-mean-squares and the adaptive rectangular-window least-mean-squares algorithms for noise suppression. The results from the application of these algorithms to synthesized images and natural scenes are presented along with mean-squared-error measures. The new algorithm performs better than the other two methods both in terms of visual presentation and mean-squared error.     

基于字典算法的OCT图像散斑稀疏降噪

光学相干层析扫描(OCT)作为一种新型无创高分辨率扫描方式,在临床上得到广泛应用,但是OCT图像本身存在严重的散斑噪声,这大大影响了疾病的诊断。本文针对OCT图像中的乘性散斑噪声,改进了两种原始字典降噪算法。该算法首先对OCT图像进行对数变换,采用正交匹配追踪算法进行稀疏编码,以及K奇异值分解学习算法进行自适应字典的更新,最后通过加权平均以及指数变换回到空域。实验结果表明,本文改进的两种字典算法能有效降低OCT图像中的散斑噪声,获得良好的视觉效果。并通过均方误差(MSE)、峰值信噪比(PSNR)、结构相似性(SSIM)以及边缘保持指数(EPI)四个指标评价降噪效果,与两种原始字典降噪算法和传统滤波算法相比,两种改进字典算法降噪效果优于其他算法,其中自适应字典算法表现更好。     

Image correction scheme applied to functional diffuse optical tomography scattering images

We have extended our investigation on the use of a linear algorithm for enhancing the accuracy of diffuse optical tomography (DOT) images, to include spatial maps of the diffusion coefficient. The results show that the corrected images are markedly improved in terms of estimated size, spatial resolution, two-object resolving power, and quantitative accuracy. These image-enhancing effects are significant at expected levels of diffusion-coefficient contrast in tissue and noise levels typical of experimental DOT data. Overall, the types and magnitudes of image-enhancing effects obtained here are qualitatively similar to those seen in previous studies on mu(a) perturbations. The implications for practical implementations of DOT time-series imaging are discussed.     

Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images

Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.     

Signal evaluation for high-speed confocal measurements

The confocal-detection principle is open especially for use in medical applications. For inspection systems applications for technical objects in reflection confocal setups are of growing importance. For such applications the confocal measurements need to have a very short measuring time. A fast detection system is needed and to satisfy this requirement only a small number of height levels are measured and a fast-evaluation algorithm is used. Drawbacks of the reduction of height levels are a greater influence of noise and additional systematic errors on the measured heights. Study the effects of the reduction are calculated, different evaluation algorithms are analyzed, and the optimization of the parameters is discussed.     

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.     

Improving performance of wavelet-based image de-noising algorithm using complex diffusion process

Abstract

Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the de-noised images in comparison to the existing methods known from the literature. The improvement is obtained without excessive computational cost, and the algorithm works well on a wide range of different types of noise.     

Enhanced three-dimensional reconstruction from confocal scanning microscope images. II. Depth discrimination versus signal-to-noise ratio in partially confocal images

The enhanced depth discrimination of a confocal scanning optical microscope is produced by a pinhole aperture placed in front of the detector to reject out-of-focus light. Strictly confocal behavior is impractical because an infinitesimally small aperture would collect very little light and would result in images with a poor signal-to-noise ratio (SNR), while a finite-sized partially confocal aperture provides a better SNR but reduced depth discrimination. Reconstruction algorithms, such as the expectationmaximization algorithm for maximum likelihood, can be applied to partially confocal images in order to achieve better resolution, but because they are sensitive to noise in the data, there is a practical trade-off involved. With a small aperture, fewer iterations of the reconstruction algorithm are necessary to achieve the desired resolution, but the low a priori SNR will result in a noisy reconstruction, at least when no regularization is used. With a larger aperture the a priori SNR is larger but the resolution is lower, and more iterations of the algorithm are necessary to reach the desired resolution; at some point the a posteriori SNR is lower than the a priori value. We present a theoretical analysis of the SNR-toresolution trade-off partially confocal imaging, and we present two studies that use the expectationmaximization algorithm as a postprocessor; these studies show that a for a given task there is an optimum aperture size, departures from which result in a lower a posteriori SNR.     

Continuous delay estimation with polynomial splines

Delay estimation is used in ultrasonic imaging to estimate blood flow, determine phase aberration corrections, and to calculate elastographic images. Several algorithms have been developed to determine these delays. The accuracy of these methods depends in differing ways on noise, bandwidth, and delay range. In most cases relevant to delay estimation in ultrasonics, a subsample estimate of the delay is required. We introduce two new delay algorithms that use cubic polynomial splines to continuously represent the delay. These algorithms are compared to conventional delay estimators, such as normalized cross correlation and autocorrelation, and to another spline-based method. We present simulations that compare the algorithms' performance for varying amounts of noise, delay, and bandwidth. The proposed algorithms have better performance, in terms of bias and jitter, in a realistic ultrasonic imaging environment. The computational requirements of the new algorithms also are considered.     

Enhanced frequency-domain optical image reconstruction in tissues through total-variation minimization

Optical image reconstruction in heterogeneous turbid media is sensitive to noise, especially when the signal-to-noise ratio of a measurement system is low. A total-variation-minimization-based iterative algorithm is described in this paper that enhances the quality of reconstructed images with frequency-domain data over that obtained previously with a regularized least-squares approach. Simulation experiments in an 8.6-cm-diameter circular heterogeneous region with low- and high-contrast levels between the target and the background show that the quality of the reconstructed images can be improved considerably when total-variation minimization is included. These simulated results are further verified and confirmed by images reconstructed from experimental data by the use of the same geometry and optically tissue-equivalent phantoms. Measures of imaging performance, including the location, size, and shape of the reconstructed heterogeneity, along with absolute errors in the predicted optical-property values are used to quantify the enhancements afforded by this new approach to optical image reconstruction with diffuse light. The results show improvements of up to 5 mm in terms of geometric information and an order of magnitude or more decrease in the absolute errors in the reconstructed optical-property values for the test cases examined.     

A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

The superpixel segmentation has been widely applied in many computer vision and image process applications. In recent years, amount of superpixel segmentation algorithms have been proposed. However, most of the current algorithms are designed for natural images with little noise corrupted. In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise, we propose a noise-resistant superpixel segmentation (NRSS) algorithm in this paper. In the proposed NRSS, the spectral signatures are first transformed into frequency domain to enhance the noise robustness; then the two widely spectral similarity measures-spectral angle mapper (SAM) and spectral information divergence (SID) are combined to enhance the discriminability of the spectral similarity; finally, the superpixels are generated with the proposed frequency-based spectral similarity. Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels. Moreover, the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering (SLIC), where the comparison results prove the superiority of the proposed superpixel segmentation algorithm     

Multiscale gradient domain compression for astronomical high dynamic range imaging

Visualisation of high dynamic range images requires compression of the data to be properly displayed on media with more limited dynamic ranges. Astronomical images pose a difficult challenge for dynamic range compression algorithms, due to the nature of the imaged objects and to the lack of a reflectance illumination model based on spatial frequencies. As a result, most of the algorithms commonly used for daylight high dynamic range compression fail in achieving an optimal visualisation of astronomical targets. We propose an extended multiscale algorithm based on compression of the dynamic range in the gradient domain. Our algorithm effectively compresses the dynamic range, enhances local contrast and avoids noise amplification. This is achieved with a multiscale representation of the image and the use of luminance information. Our results show a significantly improved visualisation of astronomical images compared to the standard gradient domain compression, as well as more robustness to noise and better artefact suppression.     

Enhancement of SAR images using fuzzy shrinkage technique in curvelet domain

The synthetic aperture radar (SAR) images are mainly affected by speckle noise. Speckle degrades the features in the image and reduces the ability of a human observer to resolve fine detail, hence despeckling is very much required for SAR images. This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different frequency scales using curvelet transform, and then applies the fuzzy shrinking technique to high-frequency coefficients to restore noise-contaminated coefficients. The proposed method does not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is suppressed. The experiment is carried out on different resolutions of RISAT-1 SAR images, and results are compared with the existing filtering algorithms in terms of noise mean variance (NMV), mean square difference (MSD), equal number of looks (ENL), noise standard deviation (NSD) and speckle suppression index (SSI). A comparison of the results shows that the proposed technique suppresses noise significantly, preserves the details of the image and improves the visual quality of the image.     

Analysis and rejection of systematic disturbances in hyperspectral remotely sensed images of the Earth

We discuss the appearance of systematic spatial and spectral patterns of noise in remotely sensed images as well as the possibility of mitigating the effects of these patterns on the data. We describe the structure of two simple theoretical models that predict the appearance of patterns of noise (mainly stripe noise). Moreover, two new algorithms that have been specifically developed to mitigate the noise patterns are described. The performance of the two algorithms is assessed by use of some hyperspectral images acquired by different kinds of airborne sensor. The algorithms show an unexpected ability to reject these noise patterns.     

An advanced algorithm for partitioning and parameter estimation in local model networks and its application to vehicle vertical dynamics

In this paper, advanced concepts for the identification of complex nonlinear systems are discussed. Three major problems are addressed: The nonlinearity of the system, noise in the data upon which the model has to be built, and the potential to incorporate qualitative and quantitative prior knowledge about the system. As an integrated solution approach, local model networks (LMNs) with appropriate parameter estimation schemes are proposed. LMNs generally offer a versatile structure for the identification of nonlinear dynamic systems. In order to account for a realistic situation when noise is present both in input and output data, an equality constrained generalised total least squares algorithm for the local model parameter estimation of the LMN is presented; the incorporation of equality constraints allows to mathematically enforce desired system properties. As an application and benchmark problem, the vertical dynamics of a vehicle is considered. After training the LMN on a rough road, excellent predictions of the behaviour of the vehicle at crossing a single obstacle are obtained, thus proving the effectiveness of the proposed algorithm. It is illustrated how both the application of a proper parameter estimation scheme and the integration of system constraints systematically improve the performance of the model.     

Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging

We outline a computationally efficient image correction algorithm, which we have applied to diffuse optical tomography (DOT) image time series derived from a magnetic resonance imaging (MRI)-based brain model. Results show that the algorithm increases spatial resolution, decreases spatial bias, and only modestly reduces temporal accuracy for noise levels typically seen in experiment, and produces results comparable to image reconstructions that incorporate information from MRI priors. We demonstrate that this algorithm has robust performance in the presence of noise, background heterogeneity, irregular external and internal boundaries, and error in the initial guess. However, the algorithm introduces artifacts when the absorption and scattering coefficients of the reference medium are overestimated--a situation that is easily avoided in practice. The considered algorithm offers a practical approach to improving the quality of images from time-series DOT, even without the use of MRI priors.     

Techniques for fast and sensitive measurements of two-dimensional birefringence distributions

We propose image processing algorithms for measuring two-dimensional distributions of linear birefringence using a pair of variable retarders. Several algorithms that use between two and five recorded frames allow us to optimize measurements for speed, sensitivity, and accuracy. We show images of asters, which consist of radial arrays of microtubule polymers recorded with a polarized light microscope equipped with a universal compensator. Our experimental results confirm our theoretical expectations. The lowest noise level of 0.036 nm was obtained when we used the five-frame technique and four-frame algorithm without extinction setting. The two-frame technique allows us to increase the speed of measurement with acceptable image quality.     

End-member extraction for hyperspectral image analysis

We investigate the relationship among several popular end-member extraction algorithms, including N-FINDR, the simplex growing algorithm (SGA), vertex component analysis (VCA), automatic target generation process (ATGP), and fully constrained least squares linear unmixing (FCLSLU). We analyze the fundamental equivalence in the searching criteria of the simplex volume maximization and pixel spectral signature similarity employed by these algorithms. We point out that their performance discrepancy comes mainly from the use of a dimensionality reduction process, a parallel or sequential implementation mode, or the imposition of certain constraints. Instructive recommendations in algorithm selection for practical applications are provided.     

基于声成像模式识别的故障诊断方法研究

利用非接触式声学故障诊断技术的工作优点,发展了基于支持向量机的声成像模式识别技术,并引入到故障诊断领域。针对某种机械故障特点,在利用波束形成算法得到声像进行噪声源识别与定位的基础上,对声像进行图像处理,提取声像的纹理特征和奇异值特征,采用支持向量机进行训练分类,进而用于机械工作状态的诊断。通过仿真及实验,得到了较好的诊断效果,表明基于声像的图像特征提取技术在结合支持向量机后可应用于机械故障诊断,为声成像方法在故障诊断领域的应用提供参考。     

采用响度控制改进算法的封闭车厢主动消声

以响度为噪声主动控制目标,提出逆模型结构和延迟补偿结构相结合的LFLMS改进算法。以轿车封闭车厢为载体构建双通道主动消声系统。以发动机噪声为噪声源,用三种控制算法进行试验,结果表明LFLMS改进算法与LFLMS算法的控制效果相近,且收敛速度快;响度控制声压减小不及声压控制,但响度减小量更大,主观听觉效果更好。