Abstract
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input low dynamic range (LDR) images using optical flow before merging them, which are error-prone and cause ghosts in results. A very recent work tries to bypass optical flows via a deep network with skip-connections, however, which still suffers from ghosting artifacts for severe movement. To avoid the ghosting from the source, we propose a novel attention-guided end-to-end deep neural network (AHDRNet) to produce high-quality ghost-free HDR images. Unlike previous methods directly stacking the LDR images or features for merging, we use attention modules to guide the merging according to the reference image. The attention modules automatically suppress undesired components caused by misalignments and saturation and enhance desirable fine details in the non-reference images. In addition to the attention model, we use dilated residual dense block (DRDB) to make full use of the hierarchical features and increase the receptive field for hallucinating the missing details. The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error. Experiments on different datasets show that the proposed AHDRNet can achieve state-of-the-art quantitative and qualitative results.
Abstract (translated)
在动态场景的高动态范围（HDR）成像中，重影是由移动对象或错位引起的关键挑战。以前的方法是先用光流对输入的低动态范围（LDR）图像进行配准，然后再进行合并，这样容易出错，结果会产生重影。最近的一项研究试图通过一个具有跳跃连接的深网络来绕过光流，但是由于剧烈的移动，仍然存在重影现象。为了从源头上避免重影，我们提出了一种新的注意力引导的端到端深度神经网络（AHDRNET），以产生高质量的无重影HDR图像。与以往直接叠加LDR图像或特征进行融合的方法不同，我们使用注意模块根据参考图像指导融合。注意力模块自动抑制由错位和饱和度引起的不需要的组件，并增强非参考图像中需要的细节。除了注意力模型外，我们还使用了扩张的剩余密度块（drdb）来充分利用层次特征，增加了对缺失细节产生幻觉的接受域。该方法是一种非基于流的方法，可以避免光流量估计误差产生的伪影。对不同数据集的实验表明，所提出的AHDRnet能够实现最先进的定量和定性结果。
URL
https://arxiv.org/abs/1904.10293
