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Title: Quality photo sketch with improved deep learning structure
Author(s): Siu, Wan Chi 
Chan, Anthony Hing-Hung 
Hui, Chun Chuen 
Zhang, Wenxin 
Issue Date: 2022
Publisher: IEEE
Related Publication(s): Proceedings of the 2022 IEEE Region 10 Conference (TENCON)
Start page: 838
End page: 843
Drawing a sketched picture from realistic scene or photo is useful. In this paper, we propose a high-quality sketch generating model using deep convolutional neural network with self-attention structure. In style-transfer investigation, how to balance and retain both information details of input and style are what we want. For sketch drawing, edges or contours are the major components to form a sketch-like image. However, how to choose edges and contours are the major topics for the model to learn. Besides, keeping a small amount of texture and shadow can give a better view of a sketch result. We resolve this problem by proposing an end-to-end jump connection with elementwise multiplication instead of addition to keep texture details of the original input, which gives highlight of edges and contours for a sketch output. Experimental results show that our new design of network surpasses other state-of-the-art models in sketch details.
DOI: 10.1109/TENCON55691.2022.9978022
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

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