Integration of an adaptive cellular automaton and a cellular neural network for the impulsive noise suppression and edge detection in digital images

Sierra, Danilo Gustavo Gil; Sogamoso, Karen Vanessa Angulo; Espitia, Helbert Eduardo

doi:10.1109/colcaci.2019.8781967

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…Even though the considered cellular automata was employed in previous works [25][26][27][28], a detailed mathematical model was not addressed. Therefore, the main aspect in this paper corresponds to the mathematical description and statistical validation.…”

Section: Discussionmentioning

confidence: 99%

“…This document aims at displaying the mathematical model for an algorithm based on cellular automata to eliminate noise in digital images. The considered algorithm is implemented in [25][26][27]; however, the mathematical description of the automata dynamics operation for filtering process is not performed, which is the object of study in this paper.…”

Section: Proposal Approach and Document Organizationmentioning

confidence: 99%

“…Regarding previous works, reference [25] presents an algorithm based on cellular automata to reduce impulsive noise in digital images. Meanwhile, the papers [26,27] describe the integration of a cellular neural network and an adaptive cellular automaton for the impulsive noise reduction and edge detection in digital images; finally, the document [28] displays a process for digital images edge detection employing cellular automaton.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Numerical Validation for an Algorithm Based on Cellular Automata to Reduce Noise in Digital Images

2022

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Given the grid features of digital images, a direct relation with cellular automata can be established with transition rules based on information of the cells in the grid. This document presents the modeling of an algorithm based on cellular automata for digital images processing. Using an adaptation mechanism, the algorithm allows the elimination of impulsive noise in digital images. Additionally, the comparison of the cellular automata algorithm and median and mean filters is carried out to observe that the adaptive process obtains suitable results for eliminating salt and pepper type-noise. Finally, by means of examples, the result of the algorithm are shown graphically.

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Section: Discussionmentioning

confidence: 99%

Section: Proposal Approach and Document Organizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and Numerical Validation for an Algorithm Based on Cellular Automata to Reduce Noise in Digital Images

2022

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“…A two-stage SAID-END denoising algorithm was presented by Amandeep Singh et al [28] to tackle the performance difficulties of existing denoising algorithms. This study by Danilo Gustavo Gil Sierra [29] proposes the sequential implementation of two bioinspired computational models to reduce impulsive noise and edge identification in grayscale images. This work, according to Piyush Joshi and Surya Prakash [30], based on noise detection, proposes a new technique for evaluating image quality.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

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Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery.

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“…Moreover, the feature selection criteria used in this model could quickly eliminate a vital feature when it is located among relevant features because it is not relevant to the group. Although many learning algorithms were presented to solve this problem, there is no generic solution formulated yet [23].…”

Section: Related Workmentioning

confidence: 99%

Cellular Neural Networks Templates Learning Approach Based on Mutual Information and Firefly Algorithm for X-Ray Images de-noising

Sharaf,

E. Abu El-Soud,

M. El-Henawy

2019

Mansoura Journal for Computer and Information Sciences

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The Cellular Neural Network is a 2D array of analog processors which forms a parallel computing framework. The main key factors in this model are the values of the neighborhood of each cell, which are called templates. These templates are usually set by a domain expert in this framework to determine the optimal values of the templates. In this paper, a novel approach was proposed to discover the templates of the cellular neural networks based on mutual information and firefly optimization. The mutual information discovers the hidden pattern in the templates by measuring the similarities among cells. The firefly algorithm navigates the search space to find the optimal values of the templates. The benchmarking and validation have been performed on the ChestX-ray8, which is a real-world X-ray images dataset. The proposed method achieved significant results when compared to other meta-heuristics algorithms such as Genetic Algorithm and Particle Swarm Optimization.

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Integration of an adaptive cellular automaton and a cellular neural network for the impulsive noise suppression and edge detection in digital images

Cited by 3 publications

References 16 publications

Modeling and Numerical Validation for an Algorithm Based on Cellular Automata to Reduce Noise in Digital Images

Modeling and Numerical Validation for an Algorithm Based on Cellular Automata to Reduce Noise in Digital Images

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Cellular Neural Networks Templates Learning Approach Based on Mutual Information and Firefly Algorithm for X-Ray Images de-noising

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