Noise reduction for magnetic resonance images via adaptive multiscale products thresholding

Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.     

Noise reduction in recursive digital filters using high-order errorfeedback

The problem of solving the optimal (minimum-noise) error feedback coefficients for recursive digital filters is addressed in the general high-order case. It is shown that when minimum noise variance at the filter output is required, the optimization problem leads to set of familiar Wiener-Hopf or Yule-Walker equations, demonstrating that the optimal error feedback can be interpreted as a special case of Wiener filtering. As an alternative to the optimal solution, the formulas for suboptimal error feedback with symmetric or antisymmetric coefficients are derived. In addition, the design of error feedback using power-of-two coefficients is discussed. The efficiency of high order error feedback is examined by test implementations of the set of standard filters. It is concluded that error feedback is a very powerful and versatile method for cutting down the quantization noise in any classical infinite impulse response (IIR) filter implemented as a cascade of second-order direct form sections. The high-order schemes are attractive for use with high-order direct form sections     

Noise reduction of image sequences using motion compensation andsignal decomposition

In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filtering, nonstationarities, i.e., parts of the signal that are momentarily not stationary, can be reduced significantly. However, since not all nonstationarities can be accounted for by motion, a motion-compensated signal still contains nonstationarities. An adaptive algorithm based on order statistics is described that decomposes the motion-compensated signal into a noise-free nonstationary part and a noisy stationary part. An RLS filter is then used to filter the noise from the stationary signal. Our new method is experimentally compared with various noise filtering approaches from literature.     

彩色数字图像的广义模糊自适应滤波

提出一类适用于彩色数字图像处理的模糊自适应滤波器，具有一种广义结构，选择不同的距离函数，则支持不同形式的已有滤波器。仿真结果显示这种滤波器性能优良。     

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

In this paper, a novel technique designed for the suppression of mixed Gaussian and impulsive noise in color images is proposed. The new denoising scheme is based on a weighted averaging of pixels contained in a filtering block. The main novelty of the proposed solution lies in the new definition of the similarity between the samples of the processing block and a small window centered at the block’s central pixel. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the neighborhood of the central pixel is utilized. This measure is defined as the sum of distances in a given color space, between a pixel of the block and a certain number of most similar samples from the filtering window. The main advantage of the proposed scheme is that the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing mixed noise of high intensity and is competitive with respect to the state-of-the-art noise filtering methods.     

A fuzzy noise reduction method for color images.

A new fuzzy filter is presented for the reduction of additive noise for digital color images. The filter consists of two subfilters. The first subfilter computes fuzzy distances between the color components of the central pixel and its neighborhood. These distances determine in what degree each component should be corrected. All performed corrections preserve the color component distances. The goal of the second subfilter is to correct the pixels where the color components differences are corrupted so much that they appear as outliers in comparison to their environment. Experimental results show the feasibility of the proposed approach. We compare with other noise reduction methods by numerical measures and visual observations. We also illustrate the performance of the proposed method as preprocessing step for edge detection.     

多尺度SVR的SAR图像复原

针对合成孔径雷达图像中存在斑点噪声的缺陷,将支持向量拟合方法引入小波系数收缩策略中,提出了基于多尺度SVR(support vector regression)的SAR图像复原算法。该方法在不同尺度下选用不同的核参数。为保护边缘结构信息,首先对各小波高频子带进行SVM拟合,然后计算原始小波系数与拟合估计值的绝对差,并定义小波系数收缩准则,根据准则对小波系数进行收缩,使复原的图像能较好的保持原有图像的纹理和结构信息。实验采用真实的SAR图像,实验结果显示该方法优于常规小波滤波和Lee滤波方法。     

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.     

Noise reduction in high dynamic range images

Multiple images with different exposures are used to produce a high dynamic range (HDR) image. Sometimes high-sensitivity setting is needed for capturing images in low light condition as in an indoor room. However, current digital cameras do not produce a high-quality HDR image when noise occurs in low light condition or high-sensitivity setting. In this paper, we propose a noise reduction method in generating HDR images using a set of low dynamic range (LDR) images with different exposures, where ghost artifacts are effectively removed by image registration and local motion information. In high-sensitivity setting, motion information is used in generating a HDR image. We analyze the characteristics of the proposed method and compare the performance of the proposed and existing HDR image generation methods, in which Reinhard et al.’s global tone mapping method is used for displaying the final HDR images. Experiments with several sets of test LDR images with different exposures show that the proposed method gives better performance than existing methods in terms of visual quality and computation time.     

Quality-efficient demosaicing for digital time delay and integration images using edge-sensing scheme in color difference domain

In this paper, we present a novel edge sensing-based demosaicing algorithm for digital time delay and integration (DTDI) mosaic images, which are captured by DTDI line-scan cameras and suitable for industrial print inspection. We propose to use Sobel- and interpolation-based masks to extract more accurate gradient information in the color difference domain. The extracted gradient information is utilized to assist the design of the proposed demosaicing algorithm. By experimenting on more than one thousand and three hundred test DTDI mosaic images, the results demonstrate the efficiency of the proposed demosaicing algorithm in terms of demosaiced image quality.     

A multiscale retinex for bridging the gap between color images andthe human observation of scenes

Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.     

Fuzzy two-step filter for impulse noise reduction from color images.

A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image.     

Noise equalization for detection of microcalcification clusters in direct digital mammogram images

Equalizing image noise is shown to be an important step in the automatic detection of microcalcifications in digital mammography. This study extends a well established film-screen noise equalization scheme developed by Veldkamp et al. for application to full-field digital mammogram (FFDM) images. A simple noise model is determined based on the assumption that quantum noise is dominant in direct digital X-ray imaging. Estimation of the noise as a function of the gray level is improved by calculating the noise statistics using a truncated distribution method. Experimental support for the quantum noise assumption is presented for a set of step wedge phantom images. Performance of the noise equalization technique is also tested as a preprocessing stage to a microcalcification detection scheme. It is shown that the square root model based approach which FFDM allows leads to a robust estimation of the high frequency image noise. This provides better microcalcification detection performance when compared to the film-screen noise equalization method developed by Veldkamp. Substantially better results are obtained than when noise equalization is omitted. A database of 124 direct digital mammogram images containing 28 microcalcification clusters was used for evaluation of the method.     

Local pixel statistics based impulse detection and hybrid color filtering for restoration of digital color images

This paper presents a two stage filtering system to remove random valued impulse noise from color images based on local statistics of the filtering window under consideration. In the first stage, to detect the noisy pixel, the locally adaptive threshold is derived from the pixels of the filtering window. In the second stage, the restoration of the noisy pixel is done on the basis of brightness and chromaticity information obtained from the neighbouring pixels in the filtering window. Simulation results show that the proposed scheme yields much superior performance in comparison with other color image filtering methods.     

Noise reduction of VQ encoded images through anti-gray coding

Noise reduction of VQ encoded images is achieved through the proposed anti-gray coding (AGC) and noise detection and correction scheme. In AGC, binary indices are assigned to the codevector in such a way that the 1-b neighbors of a code vector are as far apart as possible. To detect the channel errors, we first classify an image into uniform and edge regions. Then we propose a mask to detect the channel errors based on the image classification (uniform or edge region) and the characteristics of AGC. We also mathematically derive a criterion for error detection based on the image classification. Once error indices are detected, the recovered indices can be easily chosen from a “candidate set” by minimizing the gray-level transition across the block boundaries in a VQ encoded image. Simulation results show that the proposed technique provides detection results with smaller than 0.1% probability of error and more than 86.3% probability of detection at a random bit error rate of 0.1%, while the undetected errors are invisible. In addition, the proposed detection and correction techniques improve the image quality (compared with that encoded by AGC) by 3.9 dB     

Noise reduction for sonar images by statistical analysis and fields of experts

Sonar images are usually suffering from speckle noise which results in poor visual quality. In order to improve the sonar imaging quality, removing or reducing these speckle noises is a very important and arduous task. In this paper, the imaging principle and noise characteristics of the side-scan sonar (SSS) are analyzed, and five typical probability distribution functions are used to fit the seabed reverberation. Through experiment comparison, the Gamma distribution is selected to simulate the noise of the SSS image caused by the reverberation. Simultaneously, the fields of experts denoising algorithm based on the Gamma distribution (Gamma FoE) is proposed for SSS image denoising. In order to perceive and measure the denoising effect better, evaluation indexes of Fast Noise Variance Estimation (FNVE, an image noise estimation method) and Blind Referenceless Image Spatial Quality Evaluator (BRISQUE, an image quality evaluation method) are selected for image quality perception. The final results of the SSS image denoise experiment show that the Gamma FoE denoise algorithm has a better effect on SSS image denoise application than other denoise algorithms.     

Color fringe removal in narrow color regions of digital images

Color fringe is an artifact that should be removed in a color digital image. This paper proposes a detection method of color fringe in narrow color regions. Near-saturation region (NSR) information as well as gradient magnitudes and directions of RGB color components are used to detect transition regions, in which color fringe occurs. Four gradient directions are used in order to improve the performance of color fringe detection. NSR is used to prevent excessive detection of possible color fringe regions. Then, the detected color fringe is removed using the color difference values. Experimental results show the effectiveness of the proposed color fringe removal method.     

基于视觉特征的数字彩色图像模糊增强

对人眼的视觉特征模型进行了分析和归纳,结合多数字彩电图像增强的应用特点,提出了一种利用视觉特征模型多层边缘提取来实现数字彩电图像模糊增强的方法及其快速实现算法。     

Partition-based vector filtering technique for suppression of noise in digital color images.

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity.