An interesting article on using Deep Neural Netowrks to restore damaged biomorphic arts

Highlights • Addressing the problem of Persian pottery restoration for the first time. • Introducing a dataset containing Persian pottery images. • Employing a very recent CNN so-called partial convolution in the proposed framework. • Developing a mobile application for the restores based on the proposed framework. Abstract Conservation of the historical artworks involves carefully planning interventions to prolong the material, historical, and design integrity of humanity’s-built heritage. Among which, restoration of the destroyed parts of the artworks via inpainting is one of the most critical stages in the restorative conservation. Indeed, one of the significant assets of any art-historical work is its aesthetic aspects. However, usually due to the visual complexity and the diversity of the motifs, the number of patterns that a restorer artist can have in mind when repairing them, are limited. Also, the inpainting process of such artworks might be very slow and prone to […]

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