Feature Recognition of Abstract Art Painting Multilevel Based Convolutional Ancient Recognition Neural Network Method

Abstract

Art can be used to spread an inspirational message to encourage people and accomplish great things in life. The art of communication among people can be a mode of communication to concentrate on common issues to improve humankind. The challenging characteristics in art painting include such as degradation, cracking, and flaking is considered an essential factor. In this paper, the Multilevel Based Convolutional Ancient Recognition Neural Network Method (M-CARNNM) has been proposed to incorporate experts’ suggestions, helping artists envisage how the ancient painting may have looked after restoration. Mid-frequency analysis is introduced to reinforce the rough estimation of complete images by adding missing regions and the nearest neighbor pixels to match the maps. A domain-specific pyramid network is used to capture various space context amounts. Experimental results effectively predict the proposed method for large areas of lack of information and produce controllable vector graphics, photographic painting, and high-frequency results.