Topic
Impulse noise
About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.
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27 Jun 2011
TL;DR: A Non-Singleton Interval Type-2 (IT2) Fuzzy Logic System (FLS) for MGIN removal is posed, how it can be designed based on a Quantum-behaved Particle Swarm Optimization algorithm is explained, and it is shown that it provides both quantitatively and visually much better results.
Abstract: Removing Mixed Gaussian and Impulse Noise (MGIN) is considered to be very important in the domain of image restoration, but it is a somewhat more challenging topic than removing pure Gaussian or impulse noise. Therefore, relatively fewer works have been published in this area. This paper pro poses a Non-Singleton Interval Type-2 (IT2) Fuzzy Logic System (FLS) for MGIN removal, explains how it can be designed based on a Quantum-behaved Particle Swarm Optimization algorithm, and shows that it provides both quantitatively and visually much better results compared to other often-used non-fuzzy techniques as well as its Type-1 and singleton IT2 counterparts.
16 citations
11 May 2003
TL;DR: A modified impulse noise protection algorithm that takes advantage of the improved performance of Reed-Solomon codes when the location of the impaired bytes is known and can be reduced without compromising the immunity of the system to impulses is presented.
Abstract: The data that is transmitted in DSL system is subject to corruption by impulse noise, i.e., noise bursts of high energy that interfere with the transmitted symbols. As DSL data rates increase the crosstalk mitigation techniques become more sophisticated, impulse noise limits service in terms of rate or delay. Because of the highly non-stationary nature of impulse noise, a combination of interleaving and Reed-Solomon coding is currently used to shield systems from noise burst. This paper presents a modified impulse noise protection algorithm that takes advantage of the improved performance of Reed-Solomon codes when the location of the impaired bytes is known. Without changing the structure of the encoder or the interleaver, it is shown that the delay, or equivalently the overhead due to forward error correction coding, can be reduced without compromising the immunity of the system to impulses. A DMT-VDSL system is used as a particular example of the improvement achieved using byte-erasure.
16 citations
TL;DR: A blind CNN model for RVIN denoising with a flexible noise ratio predictor (NRP) as an indicator and results indicate that the proposed method achieves state-of-the-art performance in terms of both execution efficiency and restoration results.
Abstract: Denoising convolutional neural networks (DnCNNs), initially developed for Gaussian noise removal, are powerful nonlinear mapping models in image processing. After changes in training data, they can be used for suppression of random-valued impulse noise (RVIN) with excellent results. To achieve favorable denoising performance, however, it is necessary to have an accurate perception of the noise ratio so that the most suitable DnCNN can be chosen for denoising. Thus, this model is severely limited in flexibility. To address this problem, we propose a blind CNN model for RVIN denoising with a flexible noise ratio predictor (NRP) as an indicator. Some patches are randomly selected from the RVIN-corrupted test image, and feature vectors that indicate whether the center pixel is contaminated or not are extracted by the predictor. These feature vectors are composed of multiple statistics, namely, the multiple rank-ordered absolute differences (ROADs), the clean pixel median deviation (CPMD), and the edge pixel difference (EPD). They are rapidly mapped to noise/clean (1 for noise, 0 for clean) labels by the pre-trained noise detector (the key component of our NRP). According to the ratio of the obtained noisy labels to the total number of selected patches, the predictor provides the noise ratio of the whole image. From the output of the NRP, i.e., the predicted noise ratio, the most appropriate DnCNN specifically trained for this noise ratio is exploited for denoising. Under the guidance of the NRP, the proposed method has the ability to handle unknown noise ratios. Simulation results indicate that our blind denoising CNN model achieves state-of-the-art performance in terms of both execution efficiency and restoration results.
16 citations
TL;DR: This review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for theX-ray inspection imaging system while discriminating objects and for the screeners as well.
Abstract: In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.
16 citations
24 Mar 2014
TL;DR: The problem addressed in this paper proposes to improve the decision median filtering algorithm for denoising of video sequences corrupted with impulse noise by incorporating robust decisions to selectively operate upon the corrupted pixels.
Abstract: The recent advances in sparse representations of images have achieved outstanding results in terms of denoising and restoration; but removal of real and structured noise in digital video sequences remains a challenging problem. Based on this idea, the problem addressed in this paper proposes to improve the decision median filtering algorithm for denoising of video sequences corrupted with impulse noise. The proposed algorithm processes the extracted frame (from corrupted video sequences) by incorporating robust decisions to selectively operate upon the corrupted pixels. The local statistical parameters (of the spatial kernel) are then used to decide whether to restore the centre pixel with median value or adaptively increment the kernel size. This helps in restoration of structural content with minimal blurring at high noise densities. Experimental results show that the proposed algorithm achieves better performance with minimal computational complexity; yielding higher values of PSNR and SSIM for restored frames.
16 citations