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arXiv · 2112.13050

Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

K. Ram Prabhakar·Susmit Agrawal·R. Venkatesh Babu
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Abstract

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. The code is available at https://val.cds.iisc.ac.in/HDR/HDRRNN/index.html.

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K. Ram Prabhakar, Susmit Agrawal, R. Venkatesh Babu. 2021-12-24. Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting. https://doi.org/10.1109/tci.2021.3112920

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