论文标题
DRHDR：多支架高动态范围成像的双分支残留网络
DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging
论文作者
论文摘要
我们介绍了DRHDR，这是一个双分支残留的卷积神经网络，用于多支架HDR成像。为了解决从动态场景中融合多个括号的挑战，我们提出了一个有效的双分支网络，该网络以两种不同的分辨率运行。完整的分辨率分支使用可变形的卷积块来对齐特征并保留高频细节。一个具有空间注意力块的低分辨率分支旨在参加非参考括号中想要的区域，并抑制可能在幽灵文物上产生的流离失所的功能。通过使用双分支方法，我们能够获得高质量的结果，同时限制估计HDR结果所需的计算资源。
We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.
