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It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet\u2010based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image\u2010based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super\u2010resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg\/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.<\/jats:p>","DOI":"10.1155\/2021\/1823930","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T22:50:08Z","timestamp":1625093408000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0047-0566","authenticated-orcid":false,"given":"Hongchang","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.01.012"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12650-020-00710-6"},{"key":"e_1_2_9_3_2","first-page":"11","article-title":"Curve-structure segmentation from depth maps: a CNN-based approach and its application to exploring cultural heritage objects","volume":"32","author":"Lu Y.","year":"2018","journal-title":"Artificial Intelligence"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-019-01292-3"},{"key":"e_1_2_9_5_2","first-page":"791","article-title":"Porcelain image classification based on semi-supervised mean shift clustering","volume":"2","author":"Zhou P.","year":"2017","journal-title":"Software Engineering and Service Science"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-7998-3479-3.ch010"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2020.102963"},{"key":"e_1_2_9_8_2","first-page":"1","article-title":"A framework for design identification on heritage objects","volume":"2","author":"Zhou J.","year":"2019","journal-title":"Advanced Research Computing"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jasrep.2020.102788"},{"key":"e_1_2_9_10_2","first-page":"12","article-title":"New developments in drone\u2010based automated surface survey: towards a functional and effective survey system","volume":"2","author":"Orengo H. 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