Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.     

一种用于抑制椒盐噪声的多窗口中值滤波器

该文提出了一种用于抑制椒盐噪声的多窗口中值滤波算法。算法在招待过程中根据具体情况采用不同大小的滤波窗口。仿真结果表明，与标准中值滤波算法相比，该方法不仅可以有效去除图像中的椒盐噪声，特别是在噪声密度非常大的情况下，表现了很好的性。     

Quaternion switching filter for impulse noise reduction in color image

A novel approach to impulse noise reduction in color image is introduced in this paper. By applying the quaternion unit transform theory, the difference between two color pixels can be represented in the quaternion form. Based on the difference mentioned above, an efficient filter that can switch between the vector median filter (VMF) and the identity filter (no filtering operation) is proposed. Extensive simulation result indicates that the proposed filter achieves a trade-off between noise suppression and detail preservation in both correlated and uncorrelated impulse noise scenarios when compared with other widely used filters. Furthermore, the computational complexity analysis shows that the proposed filter is quite efficient.     

A new delayless subband adaptive filter structure

Adaptive subband techniques have been developed to reduce complexity and slow convergence problems of the traditional fullband high-order adaptive filters. Some of the disadvantages often encountered in most of the proposed architectures are the effect of aliasing associated with the multirate structure, which is a source of error in the modeling of the unknown system, and the delay introduced in the signal path. We present a new delayless maximally decimated structure where the optimal subband filters are related to the wideband system in a closed form. They make use of a special DFT analysis filterbank where the polyphase components of the prototype filter represent fractional delays so that there is no need for adaptive cross-filters, and the unknown system can modeled perfectly in a closed-loop scheme. We interpret the proposed structure as a special case of a block adaptive filter with lower computational complexity than the conventional fullband LMS algorithm. Some computer simulations are presented in order to verify the good features of the proposed structure     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.     

基于形态学和OTSU算法的红外图像降噪及分割

为了在去除红外图像的脉冲噪声的同时,有效保持和恢复图像的边缘细节,提出了基于灰度特征和众数原则的迭代双边中值滤波方法。此方法根据脉冲噪声的灰度特征以及众数原则,将取最小和最大值、而在邻域的灰度分布上孤立的像素识别为噪声。根据基于空间距离和灰度相似的加权系数,对邻域中的无噪像素与已经去噪恢复的像素进行频次加权,用频次加权中值作为噪声像素的估计值。其中,以迭代遍历的方式执行去噪处理,充分利用前次遍历处理的结果,以去除高密度噪声。实验数据证明,此方法去噪所得的PSNR和EPI值以及视觉效果均优于现有方法,具有更好的去噪性能。     

An infinite impulse response lattice filter for adaptive line enhancement

The least-squares lattice filter is used to implement an infinite-impulse-response adaptive filter for the enhancement of narrowband signals in the presence of white or finitely correlated noise. The advantages of infinite impulse response filters for adaptive line enhancement are discussed. The performance of the proposed technique is illustrated by simulation results.     

A directional coupler acoustooptic filter with reduced sidelobelevels

A two-waveguide directional coupler filter with an acoustically induced grating structure is proposed. The directional coupler filter has the advantage that it does not require input and output polarizers and does not require specific birefringent material properties. Since multiple acoustic waves could be applied simultaneously, this property could be used to couple both TE and TM polarizations from one waveguide to the other. This allows for filtering the required optical wavelength from both polarizations simultaneously. A notable weakness of acoustooptical filters and all mode-coupling filters is the high sidelobe levels in their optical transmission spectrum. A technique to suppress these high sidelobe levels in the proposed acoustooptic filter is presented. It is based on simultaneous application of several acoustic waves on the optical waveguide. This technique could be applied to any existing configuration of acoustooptic filters     

基于像素梯度自适应迭代中值滤波器的图像脉冲噪声抑制算法

图像脉冲噪声移除是获得高质量图像的关键。本文通过热红外相机成像原理研究,提出了一种基于像素梯度自适应迭代中值滤波器的图像脉冲噪声抑制算法。首先,根据相机的调制传递函数计算获取原始图像的最大像素梯度,继而建立相应的像素梯度集合。然后,计算原始图像与对应像素梯度滤波图像的梯度权重均方根误差集合,并将该集合高斯分布的最大值对应的像素梯度确定为最佳像素梯度。最后,根据图像中脉冲噪声的密度和复杂度,确定所提滤波器的自适应窗口大小和迭代次数。大量实验结果表明,所提滤波器对移除8位、16位的单通道脉冲噪声图像展现出良好的鲁棒性。与其它先进方法相比,该方法可以实时移除真实热红外相机采集图像中低密度的随机值脉冲噪声和SAPN,并实现噪声抑制过程中99.5%以上的原始像素不会遭受破坏。除此之外,针对高密度SAPN抑制,该方法获得了具有竞争力的结果,与运行时间较快的滤波方法相比表现出较好的PSNR和SSIM,与PSNR和SSIM较优秀的去噪方法相比表现出较快的运行时间。对于极限SAPN（99%）破坏的图像,也能够恢复有意义的图像细节。     

A switching median filter with boundary discriminative noise detection for extremely corrupted images.

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups--lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy--in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.     

A stochastic gradient adaptive filter with gradient adaptive stepsize

The step size of this adaptive filter is changed according to a gradient descent algorithm designed to reduce the squared estimation error during each iteration. An approximate analysis of the performance of the adaptive filter when its inputs are zero mean, white, and Gaussian noise and the set of optimal coefficients are time varying according to a random-walk model is presented. The algorithm has very good convergence speed and low steady-state misadjustment. The tracking performance of these algorithms in nonstationary environments is relatively insensitive to the choice of the parameters of the adaptive filter and is very close to the best possible performance of the least mean square (LMS) algorithm for a large range of values of the step size of the adaptation algorithm. Several simulation examples demonstrating the good properties of the adaptive filters as well as verifying the analytical results are also presented     

A method for detection of signals using frequency-domain adaptive filter

The detection performance of the frequency-domain adaptive filter is compared with the time-domain one. Comparisons are based on the computer digital simulation with a sinusoidal signal and white Gauss noise. The performance of the frequency and time-domain adaptive filters are shown to be the same for an unknown frequency signal, but for the detection of known frequency the frequency-domain adaptive filter length is much shorter to get the same detection probability.     

方向自适应的光子计数激光雷达滤波方法

提出一种自适应滤波方向的光子计数激光雷达点云滤波方法,定义了一种可调节主滤波方向的滤波核,通过遍历得到最佳滤波方向的密度值并剔除远离地物的噪声点,根据密度值与邻域内其它点的密度值差值剔除接近地物的噪声点。通过实验数据对算法进行了验证,结果表明算法能有效剔除与地面非常接近的噪声点,适用于低密度地物点云的滤波处理,其中植被滤波精度91.86%,地面点滤波精度97.89%。     

一种新的CMA盲多用户检测算法

该文提出了一种新的盲自适应多用户检测算法,这种算法的计算复杂度为O(NL),其中N为扩频因子,L为取样数据窗长度。仿真分析表明,该文提出的算法稳态性能好,收敛速度特别快,且能适应环境的变化,是一种较好的多用户检测算法。     

多径衰落信道下基于子空间的RLS盲多用户检测

本文在RLS盲检测算法的基础上,利用子空间的概念,构建了基于子空间的RLS多用户盲检测算法,在仅仅需要知道目标用户的特征序列和定时的条件下,自适应地估计检测向量,通过理论分析表明,改进的检测算法在运算复杂度上低于满秩RLS算法[7].仿真结果表明,改进的检测算法收敛性能优于满秩RLS算法,同时在特征序列畸变条件下表现出健壮件也远优于满秩RLS检测算法.     

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.     

High performance detection filter for impulse noise removal in images

A high performance detection (HPD) filter is proposed for impulse noise removal in images. In this approach, the noisy pixels are detected iteratively through several phases, based on a set of unique similarity criteria. Simulation results show that the HPD filter outperforms others at medium to high noise rates and suppresses impulse noise effectively while preserving image details, even thin lines.     

A monolithic adaptive filter

Reports a monolithic adaptive filter which has been realized using purely analog sampled-data MOS and CCD techniques. The filter implements a full Widrow least mean-squares algorithm over 65 data points. Central to this design is a novel, compact analog multiplier/accumulator circuit, which is presented in detail. The 65-point adaptive filter, which is cascadable, dissipates 200 mW from a 15 V supply, and operates at sample rates up to 100 kHz.     

Design of complex adaptive multiresolution directional filter bank and application to pansharpening

This paper proposes a new 2-D transform design, namely complex adaptive multiresolution directional filter bank, to represent the spatial orientation features of an input image adaptively. The proposed design is completely shift invariant and represents the input image by one low-pass and multiscale N directional band-pass subbands. Here, N represents estimated number of dominant directions present in the input image. Our design consists of two main filter bank stages. A fix partitioned complex-valued directional filter bank (CDFB) is at the core of the design followed by a novel partition filter bank stage. Fine partitioning of the CDFB subbands is used to get the adaptive nature of the proposed transform. The partitioning decision is made based on the directional significance of range of CDFB subband angle selectivity in the input image. Partition filter bank stage which nonuniformly partitions the CDFB subbands provides total N dominant direction selective subbands. Local orientation map of the input image is used to determine the dominant directions and hence N. For better sparsity properties, we design the multiresolution stage with filters having high vanishing moments and better frequency selectivity. Applicability of the proposed adaptive design is shown for pansharpening of multispectral images. Our proposed pansharpening approach is evaluated on images captured using QuickBird and IKONOS-2 satellites. Results obtained using the proposed approach on these datasets show considerable improvements in qualitative as well as quantitative evaluations when compared to state-of-the-art pansharpening approaches including transform-based methods.     

Nonreciprocal directional filter: a new multipurpose ferrite device

A new ferrite device, the nonreciprocal directional filter (n.d.f.), is proposed. Owing to its tunability and selective properties, the device can be successfully applied to channel separation, isolation and band elimination and can be used as a tuning element in microwave oscillators. Experimental data is presented illustrating the performance of a prototype n.d.f. as a diplexer.