论文标题
通过注意力和对齐网络有效地进行性高动态范围图像恢复
Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network
论文作者
论文摘要
HDR是计算摄影技术的重要组成部分。在本文中，我们提出了一个轻巧的神经网络，称为挑战NTIRE 2022 HDR TRACK 1和TRACK 2，称为有效的注意力和对齐引导的渐进式网络（EAPNET）。我们引入了一个多维轻型编码模块以提取特征。此外，我们提出了进行性扩张的U形块（PDUB），它可以是动态调整MACC和PSNR的渐进式插入模块。最后，我们使用快速和低功率功能空分模块来处理耗时的变形卷积网络（DCN），以处理未对准问题。实验表明，与最先进的方法相比，我们的方法在具有更好的MU-PSNR和PSNR的MACC上达到了约20倍。在测试阶段，我们获得了两个曲目的第二名。图1。显示了NTIRE 2022 HDR挑战的可视化结果。
HDR is an important part of computational photography technology. In this paper, we propose a lightweight neural network called Efficient Attention-and-alignment-guided Progressive Network (EAPNet) for the challenge NTIRE 2022 HDR Track 1 and Track 2. We introduce a multi-dimensional lightweight encoding module to extract features. Besides, we propose Progressive Dilated U-shape Block (PDUB) that can be a progressive plug-and-play module for dynamically tuning MAccs and PSNR. Finally, we use fast and low-power feature-align module to deal with misalignment problem in place of the time-consuming Deformable Convolutional Network (DCN). The experiments show that our method achieves about 20 times compression on MAccs with better mu-PSNR and PSNR compared to the state-of-the-art method. We got the second place of both two tracks during the testing phase. Figure1. shows the visualized result of NTIRE 2022 HDR challenge.
