Crack Detection and Images Inpainting Method for Thai Mural Painting Images

Jaidilert, Salinee; Farooque, Ghulam

doi:10.1109/icivc.2018.8492735

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…4 Shanxi Provincial Institute of Archaeology, Taiyuan 030000, China. 5 Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.…”

Section: Authors' Contributionsmentioning

confidence: 99%

“…In recent years, the study on virtual restoration of murals mostly focuses on the automatic filling method of damaged areas [4], mainly including image restoration methods based on diffusion, exemplar, sparse and deep learning, etc. For the diffusion-based method, Jaidilert et al [5], used a variety of existing variational in painting methods to inpaint the scratches in Thai murals extracted by region growing and morphological methods based on seed points provided by users. Shen et al [6], improved the morphological component analysis method based on sparse method to decompose the image into structure and texture parts, and simplified total variation algorithm and K-singular value decomposition algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

et al. 2021

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Environmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to produce a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the images restored by the proposed method are superior in variance, average gradient, information entropy and gray scale contrast comparing to the results from the traditional methods of homomorphic filtering and Gaussian stretching. The results also show the highest score in comprehensive evaluation of edges, hue and structure; thus, the method proposed can support more potential studies or sootiness removal in real mural paintings with more detailed information. The method proposed shows strong evidence that it can effectively reduce the influence of sootiness on the moral images with more details that can reveal the original appearance of the mural and improve its visual quality.

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“…4 Shanxi Provincial Institute of Archaeology, Taiyuan 030000, China. 5 Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.…”

Section: Authors' Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

et al. 2021

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“…In order to improve these shortcomings, we propose a method by which to automatically detect damaged areas in old photos and use the detection results to guide inpainting methods to automatically recover the original content of these areas. In general, damaged area detection involves finding damaged areas in objects, such as steel structures [ 8 ], murals [ 9 ], photos [ 10 , 11 ], frescoes [ 12 ], and pavements [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ], through algorithms. The methods for detecting damaged areas can be divided into traditional algorithms and deep learning algorithms depending on the development method.…”

Section: Introductionmentioning

confidence: 99%

“…Damage detection methods [ 9 , 10 , 11 , 12 ] were developed using traditional image processing techniques. Jaidilert et al [ 9 ] used seeded region growing [ 20 ] and morphology to detect cracks.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Image Damage Area Detection for Old Photo Recovery

Kuo

Wei

et al. 2022

Sensors

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Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process. Although there are a few fully automatic repair methods, they are in the style of end-to-end repairing, which means they provide no control over damaged area detection, potentially destroying or being unable to completely preserve valuable historical photos to the full degree. Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. We designed a damage detection model to automatically and correctly mark damaged areas in photos, and this damage can be subsequently repaired using any existing inpainting methods. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing.

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“…Li et al., 2016 [5], used threshold segmentation to extract the mud spots based on the characteristics of brightness, colourity and texture of the mud spots on the mural of Tang Dynasty tomb, which improved the accuracy of mud spots extraction, and used the existing Criminisi algorithm to inpaint the mud spots. Jaidilert et al, 2018 [6], used region growing and morphological methods to detect scratches in Thai murals based on seed points provided by users, which improved the accuracy of scratch extraction, and used the existing variational inpainting methods to restore the scratches. Although, the above two methods mainly focused on the detection and extraction methods of mud spots and scratches, and used existing inpainting methods to restore some simple damaged areas, there were little research on restoration algorithm.…”

Section: Introductionmentioning

confidence: 99%

Restoration Method of Sootiness Mural Images Based on Dark Channel Prior and Retinex by Bilateral Filter

Cao

Lyu

Hou

et al. 2020

Preprint

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Environmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to synthesize a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the proposed method can effectively reduce the influence of sootiness on the mural image and improve its visual quality. It can also be used to reveal the original appearance of the mural to reasonable extent.

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Crack Detection and Images Inpainting Method for Thai Mural Painting Images

Cited by 13 publications

References 12 publications

Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

Learning-Based Image Damage Area Detection for Old Photo Recovery

Restoration Method of Sootiness Mural Images Based on Dark Channel Prior and Retinex by Bilateral Filter

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