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

Poonam Pawar, Bharati Ainapure, Mamoon Rashid (), Nazir Ahmad, Aziz Alotaibi and Sultan S. Alshamrani
Additional contact information
Poonam Pawar: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
Bharati Ainapure: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
Mamoon Rashid: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
Nazir Ahmad: Department of Information System, College of Applied Sciences, King Khalid University, Muhayel 61913, Saudi Arabia
Aziz Alotaibi: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Sultan S. Alshamrani: Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Sustainability, 2022, vol. 14, issue 18, 1-19

Abstract: 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.

Keywords: machine learning; CNN; neural network; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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