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 this https URL

本文提出了一种基于递归网络的 HDR 去霓虹方法，使用卷积和递归的结构以生成视觉效果良好，没有去霓虹的 HDR 图像。该方法引入了一种新的递归单元结构，即自门内存（SGM）单元，它优于标准 LSTM 单元，同时包含更少的参数并具有更快的运行时间。 在双向设置中使用两个 SGM 单元可提高输出质量，并在不需要重新训练的情况下同时实现了对变长输入序列的可扩展性。此方法在三个公开可用数据集上在数量上比现有的 HDR 去霓虹方法具有最先进的性能。通过全面的消融实验，我们证明了所提出方法中各个组成部分的重要性。