Mixed Noise Environment Research Articles

Filtering Algorithm using Fuzzy Weights based on Noise Types in Mixed Noise Environments

Filtering Algorithm using Fuzzy Weights based on Noise Types in Mixed Noise Environments

복합 잡음 환경에서 변형된 퍼지 가중치 기반의 스위칭 필터 알고리즘

복합 잡음 환경에서 변형된 퍼지 가중치 기반의 스위칭 필터 알고리즘

High-order tensor low-rank approximation with application in color video recovery

Video denoising is an elementary but critical task in computer vision and has been widely studied in recent years. However, the existing denoising methods have inevitable drawbacks: some need to predefine rank, some ignore the local information, and most cannot deal with higher-order data. To overcome these shortcomings, we consider two high-order tensor low-rank approximation methods, aiming to achieve color video denoising in a mixed noise environment. First, we establish a high-order tensor framework. Based on this framework, high-order tensor robust principal component analysis (HRPCA) is proposed. Although HRPCA is capable of processing high-order data, there is still a loss of recovery details. Then, we develop another method called high-order tensor low-rank approximation with total variation regularization (HTV). In particular, the TV consists of frontal total variation (FTV) and global total variation (GTV), thus extending the HTV into HFTV and HGTV, respectively. Extensive experimental results of color videos show that the HRPCA and HTV are more efficient in dealing with denoising problems than other state-of-the-art methods.

Maximum Rate Scheduling With Adaptive Modulation in Mixed Impulsive Noise and Additive White Gaussian Noise Environments

This article proposes an opportunistic scheduling scheme based on adaptive modulation for users in a mixed noise environment, where some users are under additive white Gaussian noise (AWGN) and other users are exposed to impulsive noise. Unlike the scenario, where all the users are in an AWGN environment, the maximum signal-to-noise ratio (SNR) scheduler does not provide the maximum capacity if users are in a mixed noise environment. In the proposed scheduling scheme, called maximum rate scheduler, the user with the highest rate (or highest modulation order), instead of highest SNR, is selected. To evaluate the performance of the proposed scheduling scheme, the analyses of average spectral efficiency, outage probability and system capacity are provided along with a simple calculation of SNR thresholds for adaptive modulation in an impulsive noise environment satisfying target symbol error rate (SER). Simulation results illustrate that system capacity with the proposed scheduling given target SER = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> and the average spectral efficiency are improved by 156% and 124%, respectively, at SNR = 5 dB when a strong impulsive noise is present.

A Kernel Affine Projection-Like Algorithm in Reproducing Kernel Hilbert Space

A kernel affine projection-like algorithm (KAPLA) is proposed in reproducing kernel Hilbert space in non-Gaussian environments. The cost function for the developed algorithm is constructed by using the correntropy approach and Gaussian kernel to deal with nonlinear channel estimation. The devised algorithm can efficiently operate in the impulse noise. As a consequence, the proposed KAPLA algorithm provides good performance for nonlinear channel equalization in impluse-noise environments. Simulations results in different mixed noise environments verify the superior behavior of KAPLA compared to known algorithms.

A Novel Hybrid Kernel Adaptive Filtering Algorithm for Nonlinear Channel Equalization

In this paper, a novel kernel mixed error criterion (KMEC) algorithm is proposed for nonlinear system identification, which uses a combination of two different error schemes to implement a newly constructed cost function, which is realized by using a logarithmic squared error and a generalized maximum correntropy criterion (GMCC) to devise the KMEC algorithm. The proposed KMEC is derived in the context of the kernel adaptive filter and it provides good performance for identifying the nonlinear channels in different mixed noise environments in terms of the mean square error (MSE) at its steady-state and convergence performance.

A Study on Performance of Bilateral and Trilateral Filters in the Mixed Noise Environment

A Study on Performance of Bilateral and Trilateral Filters in the Mixed Noise Environment

Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels

A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of the compressive sensing (CS) concept and zero attracting (ZA) techniques and its estimating behavior is verified over sparse multi-path channels. The proposed algorithm is implemented by exerting different norm penalties on the two grouped channel coefficients to improve the channel estimation performance in a mixed noise environment. As a result, a zero attraction term is obtained from the expected l 0 and l 1 penalty techniques. Furthermore, a reweighting factor is adopted and incorporated into the zero-attraction term of the GC-MCC algorithm which is denoted as the reweighted GC-MCC (RGC-MMC) algorithm to enhance the estimation performance. Both the GC-MCC and RGC-MCC algorithms are developed to exploit well the inherent sparseness properties of the sparse multi-path channels due to the expected zero-attraction terms in their iterations. The channel estimation behaviors are discussed and analyzed over sparse channels in mixed Gaussian noise environments. The computer simulation results show that the estimated steady-state error is smaller and the convergence is faster than those of the previously reported MCC and sparse MCC algorithms.

Super Resolution Reconstruction in Mixed Noise Environment

A hybrid Super Resolution (SR) algorithm is proposed to deal with the Low Resolution (LR) images degraded by Mixed (Gaussian + Impulse) noise. The algorithm adaptively estimates and removes the impulse noise from the input LR images based on edge, geometrical & size characteristics. The fuzzy based impulse noise removal algorithm is along with adaptive sharpening filter based SR using steering kernel regression are used to obtain a HR image. The experimental results confirm the efficacy of the algorithm for different types of images at various noise densities. General Terms Super Resolution, Mixed Noise, Surveillance applications,

복합잡음 환경에서 변형된 공간 가중치 필터에 관한 연구

복합잡음 환경에서 변형된 공간 가중치 필터에 관한 연구

복합잡음 환경에서 에지 보존을 위한 영상복원

복합잡음 환경에서 에지 보존을 위한 영상복원

Adaptive filtering approaches for colour image and video restoration

An adaptive simplified model Kalman filter (ASMKF) is developed for image and video impulsive noise suppression. The ASMKF is more effective than the median filter (MF) and the robust reduced update Kalman filter (RRUKF) in suppressing the impulsive noise, especially when the signal-to-noise ratio is low. Further, a hybrid filter is presented for colour image and video restoration in a mixed noise environment, where both the impulsive and correlated noise may be present. The proposed hybrid filter is composed of two stages, where the first stage removes the impulsive noise while the second removes the correlated noise. The ASMKF is suggested for the first stage of the hybrid filter. In the second stage of the proposed hybrid filter, a discrete wavelet transform (DWT) filter formulated using a biorthogonal wavelet with adaptive shrinkage is applied on the impulsive-noise-free image obtained from the first stage. The efficacy of the developed ASMKF and the proposed hybrid filter are illustrated through implementation results on colour image and video restoration.

A novel nonlinear filter using layered neural networks

This paper proposes a novel realization of nonlinear filters suitable for the edgepreserving smoothing of an image degraded by a mixed noise environment composed of the Gaussian and impulsive noises. This filter consists of a layered neural network and a median filter. By using layered neural networks, the parameters of the proposed filter can adapt itself to the various noisy environments through the learning of a training image. The training method of the parameter of response functions is also proposed. These parameters have important effects for the performance of the proposed filters. An example is shown to illustrate the utility of the proposed filter.

Nonlinear filtering structure for image smoothing in mixed-noise environments

This paper introduces a new nonlinear filtering structure for filtering image data that have been corrupted by both impulsive and nonimpulsive additive noise. Like other nonlinear filters, the proposed filtering structure uses order-statistic operations to remove the effects of the impulsive noise. Unlike other filters, however, nonimpulsive noise is smoothed by using a maximum a posteriori estimation criterion. The prior model for the image is a novel Markov random-field model that models image edges so that they are accurately estimated while additive Gaussian noise is smoothed. The Markov random-field-based prior is chosen such that the filter has desirable analytical and computational properties. The estimate of the signal value is obtained at the unique minimum of the a posteriori log likelihood function. This function is convex so that the output of the filter can be easily computed by using either digital or analog computational methods. The effects of the various parameters of the model will be discussed, and the choice of the predetection order statistic filter will also be examined. Example outputs under various noise conditions will be given.