  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.
