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.     

Color impulsive noise removal based on quaternion representation and directional vector order-statistics

A new method for detecting and suppressing impulsive noise in color images is presented in this paper. The proposed method is a type of switching vector filters, where the impulse detection is based on the order-statistic information about the color samples in the horizontal, vertical, and diagonal directions. The new solution first uses quaternion-based representation of color differences and median deviation-based techniques to search for the edge direction with the maximum number of similar pixels, and then utilizes the samples aligning with this edge direction to judge whether the current pixel is noisy or not and control the switching between identity (no filtering) and vector median filtering actions. Extensive experimental comparisons exhibit the validity of the proposed approach by showing significant performance improvements over other well-known color image filtering techniques.     

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。     

平滑图像噪声的差值滤波法

在图像生成和通讯的过程中,总会产生随机的脉冲干扰和其他的噪声,影响图像的质 量和视觉效果。为了滤除图像的干扰噪声,现在一般是采用中值滤波法或加权平均法。但这些 方法只能在一定程度上保持图像的边缘细节,相当多的细节被抑制。为此,本文提出一种图像 平滑的差值滤波法,尽可能的滤除噪声,又更多的保留图像的细节。实验结果表明效果明显, 这在红外遥感图像处理和临床医学的辅助诊断中,有重大应用价值。     

Robust DFT-based filtering of pulse-like FM signals corrupted by impulsive noise

Filtering of pulse-like FM signals with varying amplitude corrupted by impulse noise is considered. The robust DFT calculated for overlapped intervals is used for this aim. This technique is proposed in order to decrease amplitude distortion of output signals that can be introduced by the robust DFT calculated within a wide interval including possible zero-output. The proposed algorithm is realized through the following steps. In the first stage, the robust DFT is calculated for the intervals. Filtered signals from the intervals are obtained by applying the standard inverse DFT for the robust DFTs applied to input data. In the second stage, results for different overlapped intervals are combined using the appropriate order statistics. In addition, an algorithm inspired by the intersection of the confidence intervals rule is used for adaptive selection of the interval width in the robust DFT. Algorithm accuracy is tested on numerical examples. Computational complexity analysis is also provided.     

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.     

FLANN-based adaptive threshold selection for detection of impulsive noise in images

In this paper, a novel scheme has been suggested for removing random-valued impulsive noise from images. The proposed scheme utilizes a second-order differential impulse detection followed by a recursive median filter on the corrupted pixel locations. Adaptive threshold selection from noisy image characteristics has been emphasized in this paper. A functional link artificial neural network is used for this purpose. Comparative analysis on standard images at different noise conditions shows that the proposed scheme, in general, outperforms the existing schemes.     

Impulsive noise filtering based on noise detection in corrupted digital color images

This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.This work was supported by the Korea Science & Engineering Foundation (KOSEF) under grant no. 981-0912-057-2.     

Salt and pepper noise removal in binary images using image block prior probabilities

During scanning and transmission, images can be corrupted by salt and pepper noise, which negatively affects the quality of subsequent graphic vectorization or text recognition. In this paper, we present a new algorithm for salt and pepper noise suppression in binary images. The algorithm consists of the computation of block prior probabilities from training noise-free images; noise level estimation; and the maximum a posteriori probability estimation of each image block. Our experiments show that the proposed method performs significantly better than the state of the art techniques.     

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.     

Fuzzy Directional (FD) Filter for impulsive noise reduction in colour video sequences

This paper presents a novel Fuzzy Directional (FD) Filter for suppression of impulsive noise in colour video sequences. The proposed approach consists in the estimation of fuzzy levels to detect movement and noise presence in the neighbourhood frames, permitting to preserve the edges, fine details and chromaticity characteristics in colour images and video sequences. The new framework has been justified applying commonly used objective criteria, such as, Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE) and Normalized Colour Difference (NCD), as well subjective perception by human viewer showing better performance in comparison with known methods presented in the literature.     

Using Anfis with circular polygons for impulsive noise suppression from highly distorted images

In this paper, a novel approach is presented to the restoration of images corrupted by impulsive noise (IN), with a new nonlinear IN suppression filter, entitled circular polygons based adaptive-fuzzy filter (CF). The proposed filter is based on statistical impulse detection and nonlinear filtering which uses adaptive-network-based fuzzy inference system (Anfis) as a missed data interpolant over the circular polygons and provides estimates for the original intensity values of corrupted pixels. Impulse detection is realized by using the chi-square based goodness-of-fit test, which yields a decision about the impulsivity of each pixel. Extensive simulations were realized to demonstrate the capability of CF and they reveal that the proposed filter achieves a better performance than the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, also when the images are highly corrupted by IN.     

Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images

This paper proposes a new anisotropic diffusion approach to remove the impulse noise and retain the fine details. The proposed approach contains two stages, the first stage detects the impulse noise, and the second stage removes the noisy pixel and retains the fine details of the original image. The Laplacian operator is used to fine-tune the image quality of the restored image in the anisotropic diffusion filter. The proposed approach is tested with PSNR, IEF, correlation factor, and NSER for different test images and the results are compared against existing algorithms. The simulation results show that the proposed approach gives better results than the existing denoising algorithms.     

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.     

Adaptive histogram-based filter for image restoration

A novel filter for image restoration is proposed in this paper. The filter estimates histogram of original image via input image. It gets a membership function through the histogram, and the membership function contains a lot of information of original image. Then a weighted fuzzy mean filter is established based on this membership function; meanwhile, the filter adaptively adopts different filter scale according to the character divergence of image region and intensity of impulsive noise. Experimental result shows that new filter gives superior performance to conventional filters and currently used fuzzy filter.     

Impulsive noise detection by second-order differential image and noise removal using adaptive nearest neighbourhood filter

A novel impulsive noise detection method based on the principle that the difference between the noisy pixel and the nearest good pixel will be different from the difference between two nearby good pixels. This is achieved by constructing a second-order differential image. Three new noise removal methods are presented. Simulated results show that the proposed filter gives far better results than many existing filters and is comparable to the results obtained by JM filter based on Jarque-Bera test. Our noise detection method is computationally simpler.     

Similar neighbor criterion for impulse noise removal in images

In this paper, a new method is proposed for removing and restoring random-valued impulse noise in images. This approach is based on a similar neighbor criterion, in which any pixel to be considered as an original pixel it should have sufficient numbers of similar neighboring pixels in a set of filtering windows. Compared with other well known methods in the literature, this technique achieves superior performance in restoring heavily corrupted noisy images. Furthermore, it has low computational complexity, and equally effective in restoring corrupted color and gray-level images.     

改进自适应中值滤波的图像去噪

传统自适应中值滤波的最大最小窗口尺寸固定,并且其最大最小窗口相差较大时,运算时间较长,去噪效果并小一定最佳。本文针对传统自适应中值滤波算法的不足,提出了改进自适应中值滤波算法,首先根据椒盐噪声的分布特点,从单幅含椒盐噪声图像中估算出椒盐噪声的浓度,并分析噪声浓度与自适应中值滤波窗口尺寸之间的关系,建立它们之间的函数关系一其次根据噪声浓度确定自适应中值滤波的最大最小窗口尺寸,最后对图像进行自适应中值滤波：实验结果表明本文算法运算时间随着噪声浓度的变化而变化,而且从PSNR角度来看本文去噪效果比传统自适应中值滤波效果较好。     

Design of multi-antenna relaying for OFDM in impulsive noise environment

A low-complexity multi-antenna relaying scheme is proposed for Orthogonal Frequency Division Multiplexing (OFDM) in the presence of Class-A Impulsive Noise (IN). One way and two way relaying are considered. The signal is transmitted and received by two terminal nodes, each with a single antenna in two time phases. In the proposed design, the processing at the relay consists of Maximal-Ratio Combining (MRC) or Power-based Selection Combining (PSC) for receive combining, Amplify and Forward (AF) for power scaling, and Space Time Block Coding (STBC) for transmit diversity. Channel State Information (CSI), Discrete Fourier Transform (DFT), and Inverse Discrete Fourier Transform (IDFT) are not needed. The Selective Mapping (SLM) technique is used at the transmitter to reduce the Peak-to-Average Power Ratio (PAPR) of the OFDM signal. Then, at the receiver, the clipping technique is used to reduce the impulses that result from the impulsive noise. The proposed system reduces the complexity of the conventional system, which uses multi-relay with a single antenna. Simulation results show that the Bit Error Rate (BER) of the proposed scheme outperforms that of the conventional scheme due to the diversity inherent in the proposed scheme.