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Syst."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and\/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep learning models has been suggested for super-resolution, being either generalized approaches or application specific. Considering the crucial challenges in shoeprint based processing and lacking specific algorithms, we proposed a deep learning based GUV-Net model for high-fidelity shoeprint generation. GUV-Net imitates learning features from VAE, U-Net, and GAN network models with special treatment of absent ground truth shoeprints. GUV-Net encodes efficient probabilistic distributions in the latent space and decodes variants of samples together with passed key features. GUV-Net forwards the learned samples to a refinement-unit proceeded to the generation of high-fidelity output. The refinement-unit receives low-level features from the decoding module at distinct levels. Furthermore, the refinement process is made more efficient by inverse-encoded in high dimensional space through a parallel inverse encoding network. The objective functions at different levels enable the model to efficiently optimize the parameters by mapping a low quality image to a high-fidelity one by maintaining salient features which are important to forensics. Finally, the performance of the proposed model is evaluated against state-of-the-art super-resolution network models.<\/jats:p>","DOI":"10.1007\/s40747-021-00558-9","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T17:02:21Z","timestamp":1634835741000},"page":"933-947","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GUV-Net for high fidelity shoeprint generation"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8303-8351","authenticated-orcid":false,"given":"Muhammad","family":"Hassan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-0708","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Pang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3171-4001","authenticated-orcid":false,"given":"Di","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Daixi","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0013-1281","authenticated-orcid":false,"given":"You","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"issue":"04","key":"558_CR1","doi-asserted-by":"publisher","first-page":"1950080","DOI":"10.1142\/S0218348X19500804","volume":"27","author":"M Acevedo Mosqueda","year":"2019","unstructured":"Acevedo Mosqueda M, Acevedo Mosqueda M, Carreno Aguilera R, Martinez Zu\u00f1iga F, Pacheco Bautista D, Pati\u00f1o Ortiz M, Yu W (2019) Computational intelligence for shoeprint recognition. 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