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

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.

Applied Artificial Intelligence for Sustainability

“…In an exploration of innovations in digital image processing, Pawar et al [2] delve into the complexities of image contamination caused by various factors such as atmospheric conditions and device limitations. By employing advanced image processing techniques, these researchers emphasize the crucial significance of accurate noise detection for subsequent restoration.…”

Current progress on murals: distribution, conservation and utilization

“…The distortions can lead to mistakes in mural identification and classification, while traditional restoration of damaged artworks requires talented artisans, which are difficult to find these days. Therefore, virtual restoration of the murals may have great potential for mural conservation and research [ 17 , 135 ]. Using computer technology, digitally repaired damaged murals can be helpful for the virtual display of the murals, and it has been shown that cloud edge computing could be a useful tool to automatically identify and repair the cracks of murals [ 136 ].…”

“…It has been proposed that a multi-path convolutional neural network, which takes multiple lighted image patches as inputs and outputs the corresponding label of the center pixel, can rapidly detect mural deterioration [ 144 ]. Recently, machine learning methods have been widely for murals study, which can improve the accuracy of composition detection [ 16 , 17 ]. In addition, based on pigments and physical strata of the murals from multi-band imaging techniques, it is possible to virtually reconstruct the original color and appearance of the faded murals.…”

“…Meanwhile, new materials and methods have been also developed for the conservation of murals [ 13 – 15 ]. In addition to on-site study and conservation measures, machine learning [ 6 , 16 ], deep learning [ 17 , 18 ], and virtual reality methods [ 19 – 21 ] are widely used for the detection and conservation of murals with the rapid development of computer science and technology. These progresses greatly improve our ability to study and conserve murals although the modern restoration of murals is still time-consuming and complicated work.…”

Defog Artificial Intelligence Glasses: Neural Networks for the Imperfect Real World

“…For example, in the field of medical imaging, obtaining pristine medical scans is often limited by equipment availability and financial constraints (Trepani et al 2021). Similarly, in artistic applications, capturing flawless photographs under varying lighting conditions can be a costly endeavor (Pawar et al 2022). The industrial sector faces challenges when dealing with noisy sensor data due to equipment limitations (Radlak et al 2020).…”