Restoration method of ancient mural image defect information based on neighborhood filtering

Li, Hao

doi:10.3233/jcm-215162

Cited by 1 publication

(1 citation statement)

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“…These murals are of great interest to archaeologists and scientists for research work [5]. The preservation of these ancient images is a most challenging task, as most of these images are very old; some of these images became distorted due natural climatic disasters such as earthquakes, storms and rains [6]. It has also been noticed that improper handling of murals can also lead to distortion.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%