双迭代等距均值滤波的医学图像恢复北大核心CSCD

医学图像中往往有很多与脉冲噪声灰度相同的像素,因此含脉冲噪声的医学图像的恢复非常困难。为了获得比现有的脉冲噪声滤波器更好的噪声抑制和纹理结构保持效果,提出了一种双迭代等距均值滤波(dual iterative equidistant mean filter,DIEMF)的医学图像恢复方法。该方法采用等距离邻域进行噪声检测和去除;噪声检测器循环地利用邻域的非最值像素与中心像素之间的平均绝对差,以及利用多数原则,将噪声像素与无噪像素区分开来;噪声去除采用自适应和双迭代的方法,以等距邻域中无噪像素和先前恢复像素的平均值作为中心噪声像素的灰度估计值,充分利用最近的先前恢复的像素。实验结果表明,该方法在噪声抑制和纹理结构保持方面优于现有的方法,特别是对于低密度噪声,它比现有的滤波器具有显著的优越性。     

Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.     

Learning-based image restoration for compressed images

In this paper, we propose a novel learning-based image restoration scheme for compressed images by suppressing compression artifacts and recovering high frequency (HF) components based upon the priors learnt from a training set of natural images. The JPEG compression process is simulated by a degradation model, represented by the signal attenuation and the Gaussian noise addition process. Based on the degradation model, the input image is locally filtered to remove Gaussian noise. Subsequently, the learning-based restoration algorithm reproduces the HF component to handle the attenuation process. Specifically, a Markov-chain based mapping strategy is employed to generate the HF primitives based on the learnt codebook. Finally, a quantization constraint algorithm regularizes the reconstructed image coefficients within a reasonable range, to prevent possible over-smoothing and thus ameliorate the image quality. Experimental results have demonstrated that the proposed scheme can reproduce higher quality images in terms of both objective and subjective quality.     

Impulsive noise suppression from images with Jarque-Bera test based median filter

A novel impulsive noise (IN) eliminator filter, entitled Jarque–Bera test based Median Filter (JM), which shows a high performance at the restoration of images distorted by IN is proposed in this paper. The JM uses statistical methods in order to find out the corrupted pixels more accurately. The JM replaces only those corrupted pixels with the values obtained from standard median filter as explained in the paper. The simulation results reveal that the proposed filter shows better performance than the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise ratio is very high.     

DECONVOLUTION IN TRANSFORM-DOMAIN FOR IMAGE DATA RESTORATION

A novel scheme for image data restoration is proposed in this letter. First, a windowfunction model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally,several simulation tests are done and results prove that the algorithm is valid.     

An improved recursive and adaptive median filter for high density impulse noise

An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.     

气动光学效应图像恢复IBD算法研究

采用迭代盲目反卷积进行气动光学效应图像恢复研究,编制了相应的计算程序,获得了恢复图像和相应的气动光学效应降质过程的点扩散函数,同时讨论了共轭梯度CG算法在盲目反卷积图像恢复计算过程中的收敛性,并提出算法策略。     

一种新的图像去噪混合滤波方法

为了去除图像中混入的脉冲噪声和高斯噪声,提出了一种基于自适应中值滤波和模糊加权均值滤波的混合滤波方法.该方法首先进行噪声检测把受高斯型噪声污染的像素和受脉冲型噪声污染的像素区别开来,然后对受高斯噪声污染的像素采用模糊加权均值滤波算法,而对受脉冲噪声污染的像素则采用改进的中值滤波算法进行去噪.仿真结果证明,该方法更具有实用性和有效性.     

一种新型的图像恢复算法

针对巨型图片恢复处理过程中所面,临的运算量庞大的技术瓶颈,从光学图像退化模型出发,发展了一种基于最小二乘方的恢复算法,在算法的实现过程中回避了变换到频域的常规做法,提出了一种新颖的级联模板的运算方法,在一定程度上减小了运算量,降低了复杂度,实验结果表明该方法是有效的。     

Memristor-based genetic algorithm for image restoration

This paper explores a way of deploying the classical algorithm named genetic algorithm (GA) with the memristor. The memristor is a type of circuit device with both characteristics of storage and computing, which provides the similarity between electronic devices and biological components, such as neurons, and the structure of the memristor-based array is similar to that of chromosomes in genetics. Besides, it provides the similarity to the image gray-value matrix that can be applied to image restoration with GA. Thus, memristor-based GA is proposed and the experiment about image restoration using memristor-based GA is carried out in this paper. And parameters, such as the size of initial population and the number of iterations, are also set different values in the experiment, which demonstrates the feasibility of implementing GA with memristors.¹     

A sparsity-ranking edge-preservation filter for removal of high-density impulse noises

In this study, a novel sparsity-ranking edge-preservation filter (SREPF) is proposed for removal of high-density impulse noise in images. Using the sparse matrix representation, the first stage of SREPF is not only to identify the noisy candidates but also to decide the processing order of them via a rank of noise-pixel sparsity in the working window. Then the second stage of SREPF utilizes a modified double Laplacian convolution to confirm the truly noisy pixels and yield a directional mean to recover them. This new approach has achieved more remarkable success rate of the edge detection than other edge-preservation methods especially in high noise ratio over 0.5. As a result, SREPF has significant improvements in terms of edge preservation and noise suppression exhibited by the peak signal-to-noise ratio (PSNR) and the structural similarity index metric (SSIM). Simulation results show that this method is capable of producing better performance compared to several representative filters.     

Transformed denoising autoencoder prior for image restoration

Image restoration problem is generally ill-posed, which can be alleviated by learning image prior. Inspired by the considerable performance of utilizing priors in pixel domain and wavelet domain jointly, we propose a novel transformed denoising autoencoder as prior (TDAEP). The core idea behind TDAEP is to enhance the classical denoising autoencoder (DAE) via transform domain, which captures complementary information from multiple views. Specifically, 1-level nonorthogonal wavelet coefficients are used to form 4-channel feature images. Moreover, a 5-channel tensor is obtained by stacking the original image under the pixel domain and 4-channel feature images under the wavelet domain. Then we train the transformed DAE (TDAE) with the 5-channel tensor as the network input. The optimized image prior is obtained based on the trained autoencoder, and it is incorporated into an iterative restoration procedure with the aid of the auxiliary variable technique. The resulting model is affiliationed by proximal gradient descent technique. Numerous experiments demonstrated that the TDAEP outperforms a set of image restoration benchmark algorithms.     

单帧振动模糊图像的快速恢复算法

振动退化图像的恢复算法面临的主要问题是在比较宽松的约束条件下对模糊图像的快速恢复。在对国内外的振动模糊图像恢复算法进行充分研究的前提下,提出一种快速有效的算法。首先应用伪白化叠加的方法得到与点扩展函数对应的调制传递函数,然后根据最小相位的理论,解线性方程组得到点扩展函数。在较少的约束条件下避免了传统算法循环迭代计算复杂、费时等缺点,实验结果表明,恢复效果较好,抗噪声能力强。     

基于同态滤波与直方图均衡化的射线图像增强

针对同态滤波与直方图均衡化单独进行X射线图像增强时存在的不足,提出了在频域内将同态滤波与直方图均衡化结合使用的思想.首先,对X射线图像进行同态滤波的分频处理;再将得到的低频分量进行全局的直方图均衡化处理;最后,将高频分量跟低频分量进行线性融合.实验结果表明,经过该方法处理的X射线图像,边缘信息更加突出,且整体视觉效果更明亮清晰.通过分析均方根误差和信噪比数据,也证实了该方法能有效地增强X射线图像.     

旋转运动空间可变模糊图像的代数复原

提出了一种基于最小二乘和空间相关约束沿模糊路径卷积的代数复原方法。为了有效和快速地恢复角点处的图像信息,采用了最优估值理论。在微机上对算法进行了验证,实验结果表明,算法能有效地去除旋转运动的空间可变模糊。     

Raised cosine function for image restoration

In this paper, a new set of raised cosine functions is proposed. These functions have all the useful properties of the spline functions with the additional advantage of continuous infinite derivatives as against only a finite number of derivatives in case of the spline functions. Because of this property they exhibit a smoother behaviour. The property of smoothness, coupled with convolution, makes the raised cosine functions readily applicable to the image restoragtion problem, where the degradation is through a shift invariant blurring function. The results confirm the superior behaviour of these functions in comparison to spline functions.     

属性直方图及其在声纳图像模糊增强中的应用

提出了属性直方图的概念,分析了一种模糊增强算法的实质,并将它们应用于沉底小目标声纳图像增强过程,给出了模糊增强的实现过程和实验结果。直方图是图像处理中最重要的基本概念之一,该文提出的属性直方图的概念,是对图像处理中直方图概念的发展。     

基于直方图的自适应图像去噪滤波器

对于那些明显偏离高斯型白噪声的加性噪声,如拖尾脉冲噪声,高斯脉冲噪声等,已有方法的滤噪性能会严重退化.为此,该文提出了一种去除脉冲噪声的新方法.该方法首先由被污染图像估计出原图像的直方图.然后应用模糊集理论,利用加权策略得到了一个符合图像灰度分布统计规律的模糊隶属度函数,以此隶属度函数构建一个加权平均滤波器. 新方法有效地利用了原图像的先验知识,能够根据图像区域特性差异及脉冲噪声强弱自适应地采用不同的滤波尺度.文章比较了传统滤波器、已有的模糊滤波器和本文方法的结果.实验表明本文方法具有更好的效果.     

RLS-type two-dimensional adaptive filter with a t-distribution assumption

In this paper we propose a novel two-dimensional adaptive filter based on assumption that the error signal has a t-distribution probability distribution with α degree of freedom to reduce the effect of large amplitude errors. We apply a large weighting function for error signals which have small amplitude and small weighting function for errors, which have large amplitude. By doing so, the effect of the large amplitude error signal to the obtained adaptive parameter can be suppressed. The parameter of the adaptive system is solved using an RLS-like adaptive algorithm. The proposed adaptive algorithm was applied to enhance images that are contaminated with additive noise. The ability of small α t-distribution assumption to reduce various additive noise is higher than that by using large α. The obtained noise reduction (NR) by utilizing small α is even higher than that obtained by applying the recently proposed LMS L-filter. Furthermore, the proposed adaptive algorithm has been applied for image linear prediction. By applying small α, we can obtain lower entropy than that by utilizing large α.