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Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

Kathirvel, RP and Agrawal, S and Radhakrishnan, VB (2021) Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting. In: IEEE Transactions on Computational Imaging, 7 . pp. 1228-1239.

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Official URL: https://doi.org/10.1109/TCI.2021.3112920


We propose a novel recurrent network-based HDR deghosting method for fusing arbitrary length dynamic sequences. The proposed method uses convolutional and recurrent architectures to generate visually pleasing, ghosting-free HDR images. We introduce a new recurrent cell architecture, namely Self-Gated Memory (SGM) cell, that outperforms the standard LSTM cell while containing fewer parameters and having faster running times. In the SGM cell, the information flow through a gate is controlled by multiplying the gate's output by a function of itself. Additionally, we use two SGM cells in a bidirectional setting to improve output quality. The proposed approach achieves state-of-the-art performance compared to existing HDR deghosting methods quantitatively across three publicly available datasets while simultaneously achieving scalability to fuse variable length input sequence without necessitating re-training. Through extensive ablations, we demonstrate the importance of individual components in our proposed approach.

Item Type: Journal Article
Publication: IEEE Transactions on Computational Imaging
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: computational photography; convolutional neural networks; deghosting; exposure fusion; High dynamic range image fusion
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 06 Jun 2023 10:09
Last Modified: 06 Jun 2023 10:09
URI: https://eprints.iisc.ac.in/id/eprint/81817

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