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。     

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.     

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.     

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.     

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。     

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.     

A dual protection scheme for impulsive noise suppression in OFDM systems

Orthogonal frequency division multiplexing (OFDM) can be susceptible to impulsive noise arising from numerous sources in a noisy communications environment. Conventional Reed–Solomon (RS) codes are particularly useful for burst-error corrections and have been employed in OFDM systems to manage impulsive noise. The performance gains, however, have been somewhat limited given the sensitivity to other noise types typically present in a noisy channel. In this regard, a novel scheme utilizing a time-domain pre-processing mean filter in combination with RS coding is proposed for impulsive noise suppression in OFDM systems. This scheme is split into two stages. In the first stage, a proposed mean filter effectively detects and removes the impulsive noise using the measured statistics of the impulsive noise. In contrast to a conventional blanking type filter, the traditional mean replacement value is replaced by a composite comparison value (CCV). This principle creates a more accurate estimate of the original OFDM signal after impulsive noise removal. The residual impulsive noise is then managed by a RS decoder in the second stage. Our results show that this dual faceted approach improves OFDM performance when compared to filtering and coding techniques alone.     

Iterative non-local means filter for salt and pepper noise removal

Salt and Pepper noise (S&P noise) removal is an active research area in digital image processing. Existing techniques commonly use the local statistics within a neighborhood to estimate the centered noisy pixel, and tend to damage image details due to the image local diversity singularity and non-stationarity. To address this problem, in this paper, iterative nonlocal means filter (INLM) is proposed to exploit the image non-local similarity feature in the S&P noise removal procedure. Moreover, the proposed iterative framework update the similarity weights and the estimated values for higher accuracy. The experimental results show that the proposed INLM produces better results than state-of-art methods over a wide range of scenes both subjectively and objectively, and it is robust to the detection results.     

A low noise amplifier with automatically Q-tuned notch filter

A low noise amplifier with automatically Q-tuned notch filter is proposed. The automatic Q tuning is achieved by an analog-digital mixed circuit, in which the successive approximation register algorithm is used to search for the appropriate current value through the resonator so that the losses of the resonator are perfectly cancelled to get a deep notch.     

A two-stage shearlet-based approach for the removal of random-valued impulse noise in images

In this paper, we introduce a novel two-stage denoising method for the removal of random-valued impulse noise (RVIN) in images. The first stage of our algorithm applies an impulse-noise detection routine that is a refinement of the HEIND algorithm and is very accurate in identifying the location of the noisy pixels. The second stage is an image inpainting routine that is designed to restore the missing information at those pixels that have been identified during the first stage. One of the novelties of our approach is that our inpainting routine takes advantage of the shearlet representation to efficiently recover the geometry of the original image. This method is particularly effective to eliminate jagged edges and other visual artifacts that frequently affect many RVIN denoising algorithms, especially at higher noise levels. We present extensive numerical demonstrations to show that our approach is very effective to remove random-valued impulse noise without any significant loss of fine-scale detail. Our algorithm compares very favourably against state-of-the-art methods in terms of both visual quality and quantitative measurements.     

一种有效的混合噪声滤波算法

以噪声特点和图像结构分析为基础,提出了一种有效的混合噪声滤除算法。算法首先通过极值判断和像素间的相容性检测,分离出脉冲噪声并以中值滤波滤除;然后对含有高斯噪声的图像以模糊滤波算法进行降噪处理。实验结果表明,本算法能有效地滤除图像中脉冲与高斯混合噪声,且较好地保护了图像细节特征。     

Fast switching based median–mean filter for high density salt and pepper noise removal

This paper proposes a fast switching based median–mean filter for high density salt and pepper noise in images. The extreme minimum value and extreme maximum value of the noisy image are used to identify the noise pixels. In the filtering stage, the corrupted pixel is replaced either by median value or mean value based on the number of noise free pixels in the filtering window. The qualitative and quantitative results show that the proposed filter outperforms the other switching based filters namely ACWMF, PSMF, AMF, DBA and MDBUTMF in terms of noise removal and edge preservation for noise densities varying from 10% to 90%.     

一种基于中值-模糊技术的混合噪声滤波器

结合中值与模糊滤波技术,提出了一种新的图像混合噪声滤波算法。算法将受混合噪声污染的图像分为脉冲噪声点集与含有高斯噪声的像素点集两部分,首先进行灰度极值检测,进而借助邻域纹理信息准确检测出脉冲噪声,并以中值滤波滤除;对于含有高斯噪声的像素点则采用一种保护细节的模糊滤波器进行处理。实验结果说明算法不仅能有效地滤除脉冲与高斯混合噪声,而且可以较好地保护图像细节。