High dynamic range (HDR) imaging is to reconstruct high-quality images with a broad range of illuminations from a set of differently exposed images. Some existing algorithms align the input images before merging them into an HDR image, but artifacts of the registration appear due to misalignment. Recent works try to remove the ghosts by detecting motion region or skipping the registered process, however, the result still suffers from ghost artifacts for scenes with significant motions. In this paper, we propose a novel Multi-scale Channel Attention guided Network (MCANet) to address the ghosting problem. We use multi-scale blocks consisting of dilated convolution layers to extract informative features. The channel attention blocks suppress undesired components and guide the network to refine features to make full use of feature maps. The proposed MCANet recovers the occluded or saturated details and reduces artifacts due to misalignment. Experiments show that the proposed MCANet can achieve state-of-the-art quantitative and qualitative results.

高动态范围（HDR）成像用于从一组不同曝光的图像中重建具有宽范围照明的高质量图像。一些现有算法在将输入图像合并为HDR图像之前先对其进行对齐，但是由于未对齐，因此会出现配准伪像。最近的工作试图通过检测运动区域或跳过注册的过程来消除重影，但是，对于具有明显运动的场景，结果仍然遭受重影伪影的困扰。在本文中，我们提出了一种新颖的多尺度信道注意力引导网络（MCANet）以解决重影问题。我们使用由膨胀卷积层组成的多尺度块来提取信息特征。通道注意块可抑制不想要的组件，并引导网络优化特征以充分利用特征图。提出的MCANet可恢复被遮挡或饱和的细节，并减少由于未对准而造成的假象。实验表明，提出的MCANet可以达到最新的定量和定性结果。