Novel Computer Tomography image enhancement deep neural networks for asphalt mixtures. (17th October 2022)

• Record Type: Journal Article
• Authors: Yang, Handuo; Huyan, Ju; Ma, Tao; Tong, Zheng; Han, Chengjia; Xie, Tianyan
• Is Part Of: Construction & building materials. Volume 352(2022)
• Publication Date: 2022-10-17
• Subjects: Asphalt mixture -- CT image enhancement -- Deep learning -- Semantic feature -- Multiple tasks; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2022.129067
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: A method for enhancing CT images of asphalt mixtures. Multiple image enhancement tasks are solved in one model. The addition of semantic information can improve processing performance. This study shows that semantic spaces have similarities. Abstract: This paper aims to solve two significant problems observed in Computer Tomography(CT) asphalt mixture images, which are 1) low-quality CT image, and 2) low efficiency, by intelligent word embedding methods. The first model is called Asphalt Mixture CT Image Enhancement Network (AMCTEN), which is featured by ignoring the semantic loss, while the second model, AMCTEN+, is feature by considering semantic loss without requiring semantic information. Two models can be integrated to realize end-to-end real-application image enhancement. Experimental results show that peak signal to noise ratio of AMCTEN and AMCTEN + in the test set increased by 79.0 % and 66.4 %, structural similarity increased by 15.1 % and 13.3 %, and mean square error decreased by 97.5 % and 95.3 %, respectively. Analysis of semantic feature spaces generated by the two models indicate that word embedding can construct an effective semantic feature space, fusing the semantic features with image features can improve the image quality, and the proposed model has superiority in multiple image enhancement tasks.