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In this paper, the Multilevel Based Convolutional Ancient Recognition Neural Network Method (M-CARNNM) has been proposed to incorporate experts\u2019 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. <\/jats:p>","DOI":"10.1142\/s0219265921410036","type":"journal-article","created":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T04:09:35Z","timestamp":1623384575000},"source":"Crossref","is-referenced-by-count":3,"title":["Feature Recognition of Abstract Art Painting Multilevel Based Convolutional Ancient Recognition Neural Network Method"],"prefix":"10.1142","volume":"22","author":[{"given":"Chaofeng","family":"Li","sequence":"first","affiliation":[{"name":"Art College, Chaohu University, Hefei, Anhui 238000, P. R. China"}]},{"given":"Ebin Deni","family":"Raj","sequence":"additional","affiliation":[{"name":"Indian Institute of Information Technology, Kottayam, India"}]}],"member":"219","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"S0219265921410036BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33820-6_8"},{"issue":"2","key":"S0219265921410036BIB002","first-page":"73","volume":"1","author":"Bashar A.","year":"2019","journal-title":"J. Artif. Intell."},{"journal-title":"IEEE Trans. Intell. Transport. 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