An Adaptive Median Filtering of Salt and Pepper Noise based on Local Pixel Distribution

Gao, Zhanyi

doi:10.2991/tlicsc-18.2018.77

Cited by 17 publications

(11 citation statements)

References 18 publications

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“…But enhancing the image or signal to a possible extent by using different signal processing methods can be done and the method must be chosen according to the application needed. Gammatone frequency cepstral coefficients (GFCC) [45] Adaptive LMS filter and ICA method, KNX technology, and LabView software tool [46] Spectral enhancement and temporal enhancement of speech signal [47] eigenspace normalization method [48] 2 Mobile audio signal Minimum-variance distortion-less response (MVDR) filter [25] 3 Image Gaussian mixture model, histogram of orientations of snow and rain [35] Modified fuzzy set filter [51] Decision-based adaptive median filter [49] Collaborative Weiner filtering [50] Improved median filter [ The median filter is best suited to reduce the noise [43]…”

Section: Discussionmentioning

confidence: 99%

Enhancing Signal in Noisy Environment: A Review

Devi¹,

Mittal

2021

Smart Innovation, Systems and Technologies

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Noise is present in all environments. In the signal processing domain also, there is a presence of noise. In any application, after processing, we need a signal which is free from noise. So to obtain a high-quality signal, elimination and reduction of noise are important. The different noises include Salt-Pepper, Gaussian, Speckle, Poisson, etc. So in order to eliminate this noise, some of the noise reduction techniques or filters can be employed in between. This survey paper presents the elimination of noise in images during duct, rain, snow, etc., elimination of noise in speech signals or mobile audio in high noisy environment such as conferences, meetings, heavy traffic conditions, and elimination of noise in biomedical signals in the presence of other noisy signals. For this purpose, many filtering techniques can be used, and depending on the filtered image obtained, we can find out which is best suited for the specific purpose.

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Section: Discussionmentioning

confidence: 99%

Enhancing Signal in Noisy Environment: A Review

Devi¹,

Mittal

2021

Smart Innovation, Systems and Technologies

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“…In our study, like the initial window size used in other studies [30, 31, 35], the initial window size was decided to be

3 \times 3

and the maximum allowable window size was selected to be

7 \times 7

based on following formula [31]:

Maximum window size = (2 N + 1) \times (2 N + 1)

where N is the initial window size ( N = 3).…”

Section: Methodsmentioning

confidence: 99%

Multistage adaptive noise reduction technique for optical resolution photoacoustic microscopy

Guezzi

Lee

et al. 2022

Journal of Biophotonics

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“…Thus, the undamaged pixel is replaced with the median value of the window. As mentioned below, when the pixel is destroyed, the impulse noise would be eliminated similarly [17].…”

Section: Adaptive Median Filtermentioning

confidence: 99%

Intelligent Deep Learning Enabled Crowd Detection and Classification Model in Real Time Surveillance Videos

Sivachandiran¹,

Mohan²,

Nazer³

2022

INDJCSE

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Recently, security surveillance applications exploited the computer vision based detection and tracking approaches to improve the safety and comfort of humans. A major concern in real time surveillance video tracking is the process of identifying the human crowd behavior and classifying them. It finds useful to alert the crow in case of any disasters and unpredicted events. The investigation of human behavior in crowded surveillance videos is an essential and crucial area of research. The recent advances in Artificial Intelligence (AI) and deep learning (DL) models can be employed for determining the crowd behavior analysis in surveillance videos. With this motivation, this article focuses on the design of intelligent deep learning enabled crowd behavior detection and classification (IDL-CBDC) model in real time surveillance videos. The goal of the IDL-CBDC technique is to detect the crowd and classify it into four classes namely marriage, political, school, and college. Primarily, the IDL-CBDC technique performs preprocessing in two levels namely adaptive median filtering (AMF) technique and contrast enhancement (CE) approach. Besides, a deep instance segmentation approach using PSPNet-101 model is used for the segmentation of input video frames into crowds. Moreover, the black widow optimization (BWO) with residual network (ResNet50) model is applied for the crowd detection and classification process. The design of BWO algorithm helps to properly adjust the hyperparameter values such as learning rate, batch size, number of epochs, and number of hidden layers. In order to ensure the improved performance of the IDL-CBDC technique, a set of simulations take place using an own dataset, gathered from public places. Extensive comparative result analysis reported the supremacy of the IDL-CBDC technique over the other techniques.

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An Adaptive Median Filtering of Salt and Pepper Noise based on Local Pixel Distribution

Cited by 17 publications

References 18 publications

Enhancing Signal in Noisy Environment: A Review

Enhancing Signal in Noisy Environment: A Review

Multistage adaptive noise reduction technique for optical resolution photoacoustic microscopy

Intelligent Deep Learning Enabled Crowd Detection and Classification Model in Real Time Surveillance Videos

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