Impulse noise

About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.

Two-stage methods for mixed noise removal [image denoising]

Abstract: Summary form only given. Images are quite often corrupted by mixed additive and impulsive noise. Then, the task in the filtering is to remove both components of the noise. We compare in this paper two ways how to do this. The first one is to apply some robust order statistic filter and the other one is to use a two-stage approach where impulses are first detected and removed, and after that additive noise is suppressed. For the latter approach, two methods are proposed. We demonstrate through experiments that the latter approach performs better than several standard order statistic filtering techniques. In addition, a modified version of a method to estimate the variance of additive noise is introduced. The given method performs well enough even with mixed noise.

Quantized peak based impulsive noise blanking in powerline communications

Abstract: Many IN mitigation techniques have been proposed to mitigate impulsive noise (IN) over powerlines, the most common of which is the blanking technique. The conventional way to implement this technique however requires prior knowledge about the IN characteristics to identify the optimal blanking threshold (OBT). When such knowledge cannot be obtained the performance deteriorates rapidly. To alleviate this, a look-up table (LUT) based algorithm with uniform quantization is deployed to utilize estimates of the peak to average power ratio at the receiver to determine the OBT. In this paper, we investigate the impact of quantization bits on the system performance as well as the performance loss due to the impact of IN on the side information. Two aspects of the achievable performance are considered namely, output signal-to-noise ratio (SNR) and symbol error rate under various IN scenarios. The results reveal that a 5 bit LUT is sufficient to achieve a gain of up to 3dB SNR improvement relative to the conventional blanking method. Furthermore, it will be shown that the loss due to the practical impact of IN on the side information is insignificant.

Image noise removal with detail preservation for random valued impulse noise

Abstract: Edge information is the most vital high frequency information of an image, filtering an image to reduce noise while keeping the image details preserved is one of the most important issues. In this paper we proposed a method in which double median filtering stage is used to preserve edge details, which enhance the quality of image. The algorithm is carried out in two stages; first stage is detection stage which detects noisy pixels, second stage is a filtering stage in which median is calculated twice. The goal of proposed algorithm is to remove high density noise in digital images while keeping edge details. The proposed method gives better image quality with high peak signal to noise ratio (PSNR) and reduced mean square error (MSE). Results of proposed algorithm have been analyzed in terms of visual and quantitative results.

Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach

Abstract: In this work we propose a technique to remove sparse impulse noise from hyperspectral images. Our algorithm accounts for the spatial redundancy and spectral correlation of such images. The proposed method is based on the recently introduced Blind Compressed Sensing (BCS) framework, i.e. it empirically learns the spatial and spectral sparsifying dictionaries while denoising the images. The BCS framework differs from existing CS techniques - which assume the sparsifying dictionaries to be data independent, and from prior dictionary learning studies which learn the dictionary in an offline training phase. Our proposed formulation have shown over 5 dB improvement in PSNR over other techniques.