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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06 Feb 2006TL;DR: In this paper, the authors proposed a method for detecting the presence of impulse noise in a DSL transmitter receiver device by comparing a hard decision output of a decoder with a soft decision output for a convolution coded modulation symbol received at a DSL receiver.
Abstract: Methods and apparatuses for reducing effects of impulse noise in a DSL transmitter receiver device are described. According to certain embodiment, the method includes comparing a hard decision output of a decoder with a soft decision output for a convolution coded modulation symbol received at a digital subscriber line (DSL) receiver. The presence of impulse noise is detected based on a lack of agreement between the hard decision output and the soft decision output.
21 citations
TL;DR: The results imply that impact noise and whole-body vibration, at levels commonly found in industrial settings, can interact to increase the susceptibility of the chinchilla cochlea to noise-induced hearing loss.
Abstract: Monaural chinchillas were exposed for 10 days to one of these conditions: (1) whole-body vibration (30 Hz, 1 g rms acceleration); (2) impact noise (113 dB peak SPL 1/s) or (3) a combination of whole-body vibration and impact noise. Thresholds were monitored before, during and after the exposure using the auditory-evoked response or behavioral conditioning techniques. Vibration alone had essentially no effect on threshold. Exposure to impact noise produced a stable asymptotic level of threshold shift 2-8 h after exposure onset. The asymptotic threshold shift was roughly 35 and 43 dB SPL at 0.5 and 8.0 kHz, respectively. Exposure to impact noise plus vibration produced an asymptotic threshold shift at 0.5 and 8.0 kHz that was approximately 10 dB greater than noise alone. The combination group also showed greater permanent threshold shifts and greater hair cell losses than the group exposed only to impact noise. The results imply that impact noise and whole-body vibration, at levels commonly found in industr...
21 citations
11 Jun 2010
TL;DR: A new technique for detects and removes impulse noise in grayscale digital images using fuzzy reasoning with lowest uncertainty and a heuristic median filter is combined with human knowledge for select best replacement.
Abstract: In this paper we proposed a new technique for detects and removes impulse noise in grayscale digital images. Proposed method work in two steps, in first step we detect noisy pixels using fuzzy reasoning with lowest uncertainty, and in second step we replace noisy pixels with a heuristic median filter, our heuristic median's filter is combined with human knowledge for select best replacement. We analysis this method with PSNR (Pick Signal Noise Ratio) metric and visual comparison, the results show this method is very good for noise reduction and image restoration in high level noisy images.
21 citations
TL;DR: Experimental work showed that the proposed method can achieve low loss and root meansquare error during training, high peak signal to noise ratio, low mean square error, image quality assessment with good quality and mean absolute error for close prediction between denoised and original color images.
Abstract: Elimination of combined Gaussian and impulse noises in digital image processing with preservation of image details and suppression of noise are challenging problem. For this purpose, a new filter which is median filters combined with convolutional neural network for Gaussian and salt & pepper noises. The previous methods are application dependents; some used for impulse noise and other employed only for Gaussian noise. The elimination of Gaussian and impulse noise completed into two steps. First the detection of impulse noise with the rejection of noise by employed of 3 × 3 and 5 × 5 window size median filters. In the second step removal of Gaussian noise performed by residual learning denoising convolutional neural network. It is very favorable and the ability of learning and denoising performance in the field of digital image processing. Denoising convolutional neural network also has active Gaussian noise with an unknown level of noise. Experimental work showed that the proposed method can achieve low loss and root mean square error during training, high peak signal to noise ratio, low mean square error, image quality assessment with good quality and mean absolute error for close prediction between denoised and original color images.
21 citations
TL;DR: Extensive simulation results reveal that the A2FM filter not only exhibits desirable robustness in suppressing noise but also outperforms other proposed filtering approaches.
Abstract: Image detail preservation and impulse noise attenuation are difficult to achieve simultaneously in the area of image restoration design. This study proposes a novel adaptive type-2 fuzzy median (A2FM) filter to accomplish these objectives. The novel filter is based mainly on the uncertainty of the capacity of the type-2 fuzzy sets to provide the advantages of two filters: the Arakawa proposed fuzzy median (FM) filter and the general type-1 based FM (type-1 FM) filter. The proposed method of designing an A2FM filter, adopts a powerful scheme for overcoming restrictions of the membership function in the type-1 FM filter. It also comprises a flexibility inference mechanism to reduce the memory usage of the FM filter. Extensive simulation results reveal that the A2FM filter not only exhibits desirable robustness in suppressing noise but also outperforms other proposed filtering approaches.
21 citations