Image denoising using 2-D separable oversampled DFT modulated filter banks

The direction-frequency selectivity of two-dimensional (2-D) separable oversampled discrete Fourier transform (DFT) modulated filter banks, showing that such 2-D filter banks can provide fine frequency tiling and efficient image representations with direction-frequency selectivity, is analysed. Moreover, the doubly local Wiener filtering is extended to the case using two 2-D separable oversampled DFT modulated filter banks, where the empirical subband energy distributions of the image are estimated by the two sets of directional windows matching the direction-frequency selectivity of the subband filters. The proposed algorithm is of low computational complexity and exhibits a great capability to preserve inhomogeneous textures, owing to the fact that 2-D separable DFT modulated bases are suited to represent oscillating patterns in images. The experimental results show that the proposed denoising algorithm is competitive with the existing algorithms with comparable computational complexity. Particularly, for images with abundant inhomogenous texture, it gives larger output peak signal-to-noise ratios (PSNRs) and achieves better visual effect in texture regions of images.     

Palmprint feature extraction using 2-D Gabor filters

Biometric identification is an emerging technology that can solve security problems in our networked society. A few years ago, a new branch of biometric technology, palmprint authentication, was proposed (Pattern Recognition 32(4) (1999) 691) whereby lines and points are extracted from palms for personal identification. In this paper, we consider the palmprint as a piece of texture and apply texture-based feature extraction techniques to palmprint authentication. A 2-D Gabor filter is used to obtain texture information and two palmprint images are compared in terms of their hamming distance. The experimental results illustrate the effectiveness of our method.     

基于多小波基的联合图像去噪方法

小波广泛应用于图像去噪,多个小波基联合运用可以把小波的光滑性、紧支性、正交性、对称性等结合起来。首先用多个小波基分别对含噪图像进行分解、阈值处理和重构,得到多幅重构图像,然后对这些图像进行算术平均,得到最终去噪图像。实验结果表明,该方法去噪效果比单一的小波基方法有明显的改善。     

带边缘补偿的分数阶积分图像去噪算法

针对分数阶积分的图像去噪算法容易丢失图像细节特征的问题,提出了一种带边缘补偿的分数阶积分图像去噪算法。介绍了分数阶积分算子具有尖锐的低通性能,将分数阶Cauchy公式引入到数字图像去噪中,并利用斜坡法来近似计算分数阶积分的数值解。在迭代去噪的过程中,该算法在图像信噪比（SNR）上升阶段,设定较高微小积分阶次来构建去噪掩模;在图像信噪比开始下降阶段,设定较低微小积分阶次来构建去噪掩模,并采用边缘补偿机制来部分恢复图像的细节信息。由仿真实验可知,提出的图像去噪算法由于在迭代去噪的过程中采用了不同的分数阶积分阶次和边缘补偿机制,与已有的降噪算法相比,可以在去除噪声的同时适当恢复原始图像的细节信息,由此获得更高的信噪比和更佳的视觉效果。     

一种无抽样Contourlet变换的图像去噪方法

提出一种基于Contourlet变换的图像去噪方法,Contourlet变换采用无抽样形式,系数萎缩处理阈值门限与尺度相关。通过模拟产生不同方差噪声信号进行Contourlet变换,确定各尺度子带系数阈值,得到噪声方差与各尺度子带阈值对应表。对噪声污染图像进行Contourlet变换并估计噪声方差,查表得到各子带阈值,进行系数萎缩处理。实验表明提出的处理方法简单有效,去噪结果具有良好去噪视觉效果和较高峰值信噪比。     

基于多小波变换的图像去噪技术

多小波变换同时拥有正则性、正交性、紧支性、对称性等单小波不能同时满足的优点,近年来在图像处理领域受到了广泛关注。利用多小波变换后系数之间存在的大量的相关性的特点,提出了一种新的小波阈值方法。通过仿真实验,证明了该方法能有效地去除噪声。     

PDTDFB变换和双变量模型在图像去噪中的应用

对金字塔复方向滤波器组和贝叶斯最大后验估计理论架构下的双变量模型进行研究的基础上,结合二者的优点,提出一种新的图像去噪算法。PDTDFB（Pyramidal Dual-Tree Directional Filter Bank）变换具有近似时移不变性、多尺度、多方向选择性好的特点;双变量模型充分突出图像分解后系数的尺度内和尺度间的双重相关性;对噪声估计方法做出了详细阐述。仿真实验表明,与已有的多尺度理论（如：轮廓波等）和一些典型的图像去噪算法相比较,该算法的客观评价指标PSNR以及去噪后图像的主观视觉效果都有明显的提高和改善,能有效地保留原始图像的纹理和细节信息。     

平面曲线去噪的混合双边算法

介绍了一种对平面离散曲线进行去噪声的新的方法。在网格去噪算法中,有两种比较好的去噪方法,分别是由Fleishman等人与Jones等人提出的。这两种网格去噪算法都能较好地保持特征,但是它们的缺点是前者会产生收缩,而后者则会产生扩张,将这两种算法结合起来,并且应用于曲线,得到了一种新的不会产生收缩,并且能够很好保持特征的平面曲线去噪算法,较好地解决了在曲线去噪中的两个重要问题：收缩和保特征。     

Online self-repair of FIR filters

Chip-level failure detection has been a target of research for some time, but today's very deep-submicron technology is forcing such research to move beyond detection. Repair, especially self-repair, has become very important for containing the susceptibility of today's chips. This article introduces a self-repair-solution for the digital FIR filter, one of the key blocks used in DSPs.     

保特征的联合滤波网格去噪算法

目的 在去噪的过程中保持网格模型的特征结构是网格去噪领域研究的热点问题。为了能够在去噪中保持模型特征,本文提出一种基于变分形状近似（VSA）分割算法的保特征网格去噪算法。方法 引入变分形状近似分割算法分析并提取噪声网格模型的几何特征,分3步进行去噪。第1步使用变分形状近似算法对网格进行分割,对模型进行分块降噪预处理。第2步通过分析变分形状近似算法提取分割边界中的特征信息,将网格划分为特征区域与非特征区域。对两个区域用不同的滤波器联合滤波面法向量。第3步根据滤波后的面法向量,使用非迭代的网格顶点更新方法更新顶点位置。结果 相较于现有全局去噪方法,本文方法可以很好地保持网格模型的特征,引入的降噪预处理对于非均匀网格的拓扑结构保持有着很好的效果。通过对含有不同程度高斯噪声的网格模型进行实验表明,本文算法无论在直观上还是定量分析的结果都相较于对比的方法有着更好的去噪效果,实验中与对比算法相比去噪效果提升15%。结论 与现有的网格去噪算法对比,实验结果表明本文算法在中等高斯噪声下更加鲁棒,对常见模型有着比较好的去噪效果,能更好地处理不均匀采样的网格模型,恢复模型原有的特征信息和拓扑结构。     

Improved image denoising via RAISR with fewer filters

In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart phones.Accurate conventional denoising ...     

Image denoising with complex ridgelets

In this paper, we propose a novel image denoising method by incorporating the dual-tree complex wavelets into the ordinary ridgelet transform. The approximate shift invariant property of the dual-tree complex wavelet and the high directional sensitivity of the ridgelet transform make the new method a very good choice for image denoising. We apply the digital complex ridgelet transform to denoise some standard images corrupted with additive white noise. Experimental results show that the new method outperforms VisuShrink, the ordinary ridgelet image denoising, and wiener2 filter both in terms of peak signal-to-noise ratio and in visual quality. In particular, our method preserves sharp edges better while removing white noise. Complex ridgelets could be applied to curvelet image denoising as well.     

Image denoising feedback framework using split Bregman approach

In this paper, an image denoising feedback framework is proposed for both color and range images. The proposed method works on an error minimization principle using split Bregman method. At first image is denoised by computing means in the local neighborhood. The pixels that have big differences from the center of the local neighborhood compared to the noise variance are then extracted from the denoised image. There is a low correlation between the extracted pixels and their local neighborhood. This information is fed to the feedback function and denoising is performed again, iteratively, to minimize the error. In most cases, the proposed framework yields best results both qualitatively and quantitatively. It shows better denoising results than the bilateral filtering when the edge information in the input images is affected by intense noise. Moreover, during the denoising process feedback function ensures that the edges are not over smoothed. The proposed framework is applied to denoise both color and range images, which shows it works effectively on a wide variety of images unlike the evaluated state-of-the-art denoising methods.     

Eigenfilter design of linear-phase FIR digital filters using neural minor component analysis

This paper proposes a minor component analysis-based neural learning algorithm for designing linear-phase finite impulse response digital filters. The objective function to be minimized in the least-squares design can be formulated as the eigenvalue problem for solving an appropriate real, symmetric, and positive-definite matrix. To achieve the eigenfilter design, an alternative neural learning rule based on the minor component analysis algorithm is exploited. The optimal filter coefficients corresponding to the eigenvector of the smallest eigenvalue of the positive-definite matrix can be achieved in an iterative manner, avoiding the complex computation of eigenvalue decomposition. Furthermore, the learning step parameter that affects the convergence performance is investigated empirically. The simulation results indicate that the proposed neural-based approach can be applied to eigenfilter design and yields a lower computational complexity compared with traditional matrix algebraic-based approaches.     

改进的NeighLevel图像去噪方法

针对复小波变换在图像方向信息表征和NeighLevel算法刻画邻域相关性的局限性,提出了一种改进的图像去噪方法。首先,利用抗混叠轮廓波自由选择方向数的特点,能更好地提取图像边缘细节,克服了复小波方向性信息表达的不足;然后用变换域邻域小波系数之间的互信息量,改进NeighLevel方法对邻域信息的表达能力。理论分析和实验结果表明,与CWT-NeighLevel相比,在噪声方差等于30~60时,峰值信噪比提高了0.6%~7.0%,且在边缘特征方面保持了良好的视觉效果。     

解离散系数滤波器设计问题的分支定界算法

基于离散系数滤波器设计问题已有的半定规划松弛模型,利用文献[6]的方法给出了该问题的二次规划松弛模型,该模型能给出比半定规划模型更好的界,然后运用分支定界方法求解该模型。与随机扰动方法相比,该方法能得到一个性能更好的次优解,对于精度要求较高的滤波器设计问题,这种方法非常有效,并通过了仿真实验的证实。     

Image denoising with norm weighted fusion estimators

In recent era, the weighted matrix rank minimization is used to reduce image noise, promisingly. However, low-rank weighted conditions may cause oversmoothing or oversharpening of the denoised image. This demands a clever engineering algorithm. Particularly, to remove heavy noise in image is always a challenging task, specially, when there is need to preserve the fine edge structures. To attain a reliable estimate of heavy noise image, a norm weighted fusion estimators method is proposed in wavelet domain. This holds the significant geometric structure of the given noisy image during the denoising process. Proposed method is applied on standard benchmark images, and simulation results outperform the most popular rivals of noise reduction approaches, such as BM3D, EPLL, LSSC, NCSR, SAIST, and WNNM in terms of the quality measurement metric PSNR (dB) and structural analysis SSIM indices.     

A Neural Network Approach for Designing 2-D FIR Filters with Arbitrary Magnitude Responses

In this paper, a Neural Network Approach （NNA） is presented for the design of two-dimensional （2-D） Finite-Impulse Response （FIR） filters with arbitrary magnitude responses. Its main goal is to minimize the frequency-domain error function. To illustrate the feasibility of the NNA, a Back.Propagation Neural Network （BPNN） model is chosen, and the convergence and stability of the BPNN are studied. The method avoids matrix inversion operation, and makes a very fast calculation of the filter＇s coefficients possible when the learning rate of the BPNN is selected suitably. The novel feature of the proposed approximation approach is that it can be used to design any kind of 2-D linear-phase FIR filters without any symmetry constraint on their magnitude-frequency responses. Several optimal design examples are given to illustrate the effectiveness of the proposed approach.