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BITS-Net: Blind Image Transparency Separation Network

Zhou, Chao; Lyu, Zhao Yan; Rodrigues, Miguel; (2023) BITS-Net: Blind Image Transparency Separation Network. In: Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP). (pp. pp. 375-379). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

This research presents a new approach for blind single-image transparency separation, a significant challenge in image processing. The proposed framework divides the task into two parallel processes: feature separation and image reconstruction. The feature separation task leverages two deep image prior (DIP) networks to recover two distinct layers. An exclusion loss and deep feature separation loss are used to decompose features. For the image reconstruction task, we minimize the difference between the mixed image and the re-mixed image while also incorporating a regularizer to impose natural priors on each layer. Our results indicate that our method performs comparably or outperforms state-of-the-art approaches when tested on various image datasets.

Type: Proceedings paper
Title: BITS-Net: Blind Image Transparency Separation Network
Event: 2023 IEEE International Conference on Image Processing (ICIP)
Location: Kuala Lumpur, Malaysia
Dates: 8th-11th October 2023
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICIP49359.2023.10222918
Publisher version: https://doi.org/10.1109/ICIP49359.2023.10222918
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: blind image separation, deep image prior, deep learning, computer vision
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10174937
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