Automatic image cracks detection and removal on mobile devices

Voronin, Viacheslav; Marchuk, Vladimir; Sizyakin, Roman; Gapon, Nikolay; Pismenskova, Marina; Svetlana, Tokareva

doi:10.1117/12.2224339

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…After that, the found patches are used to reconstruct the damaged area. [21][22][23] The most difficult cases for this group of methods are cases when the lost area includes a semantically important object in the image. Semantically important areas can include, for example, the wheels of a car, the windows of a house, or the mouth and eyes on the face.…”

Section: Related Workmentioning

confidence: 99%

Virtual restoration of paintings using adaptive adversarial neural network

et al. 2022

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Over time, the visual quality of the paintings deteriorates. Cracks and loss of paint are the main types of damages that worsen the visual component of the painting. One of the ways to return the authentic appearance of paintings is a virtual restoration. Virtual restoration consists of two main stages: detecting deterioration and their removal. In this research, we investigate the possibility of applying deep learning-based methods for virtual restoration. To detect cracks we use a combination of convolutional (MCN) and autoencoder neural networks based on U-NET architecture, and to remove them, an adaptive adversarial network (aGAN). Also, in this work, we propose an original way of training an adversarial neural network, which allows us to apply it more successfully in practice. A series of experiments shows encouraging results compared to known methods and confirms the high efficiency of deep learning.

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Section: Related Workmentioning

confidence: 99%

Virtual restoration of paintings using adaptive adversarial neural network

et al. 2022

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“…Machine learning approaches are based on vector classification 4,5,8 or tensor classification. [9][10][11] An efficient Bayesian crack detection method 8 employed Bayesian Conditional Tensor Factorization (BCTF) 12 to detect cracks on a multimodal dataset and proved excellent results on high-resolution images of the Ghent Altarpiece.…”

Section: Related Workmentioning

confidence: 99%

A two-stream neural network architecture for the detection and analysis of cracks in panel paintings

Sizyakin

Cornelis

Meeus

et al. 2020

Optics, Photonics and Digital Technologies for Imaging Applications VI

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Museums all over the world store a large variety of digitized paintings and other works of art with significant historical value. Over time, these works of art deteriorate, making them lose their original splendour. For paintings, cracks and paint losses are the most prominent types of deterioration, mainly caused by environmental factors, such as fluctuations in temperature or humidity, improper storage conditions and even physical impacts. We propose a neural network architecture for the detection of crack patterns in paintings, using visual acquisitions from different modalities. The proposed architecture is composed of two neural network streams, one is a fully connected neural network while the other consists of a multiscale convolutional neural network. The convolutional neural network plays a leading role in the crack classification task, while the fully connected neural network plays an auxiliary role. To reduce the overall computational complexity of the proposed method, we use morphological filtering as a pre-processing step to safely exclude areas of the image that do not contain cracks and do not need further processing. We validate the proposed method on a multimodal visual dataset from the Ghent Altarpiece, a world famous polyptych by the Van Eyck brothers. The results show an encouraging performance of the proposed approach compared to traditional machine learning methods and the state-of-the-art Bayesian Conditional Tensor Factorization (BCTF) method for crack detection.

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“…Methods based on the search of self-similar patches on an undamaged area of the image cope with this problem more successfully. After that, the found patches are used to reconstruct the damaged area [16][17][18]. The most difficult cases for this group of methods are cases when the lost area includes a semantically important object in the image.…”

Section: Introductionmentioning

confidence: 99%

Virtual restoration of paintings based on deep learning

Sizyakin

Voronin

Pižurica

2022

Fourteenth International Conference on Machine Vision (ICMV 2021)

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Over time, crack pattern (craquelure) inevitably develops in paintings as a sign of their ageing, sometimes accompanied by larger losses of paint (lacunas). In restoration treatments, cracks are typically not filled in, and virtual restoration is often the only option to "reverse" the ageing of paintings, simulating their original appearance. Moreover, virtual restoration can serve as an important supporting step in decision making during the physical restoration. In this research, we investigate the possibility of applying deep learning-based methods for virtual restoration. In particular, our crack detection method is based on a convolutional autoencoder (U-Net), and we employ a generative adversarial neural network (GAN) to virtually inpaint the detected cracks. We propose an original way of training the GAN model for painting restoration, which improves its practical performance. A series of experiments shows encouraging results in comparison with known methods, and indicates huge potential of deep learning for virtual painting restoratin.

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Automatic image cracks detection and removal on mobile devices

Cited by 5 publications

References 9 publications

Virtual restoration of paintings using adaptive adversarial neural network

Virtual restoration of paintings using adaptive adversarial neural network

A two-stream neural network architecture for the detection and analysis of cracks in panel paintings

Virtual restoration of paintings based on deep learning

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