High dynamic range (HDR) imaging is an important task in image processing that aims to generate well-exposed images in scenes with varying illumination. Although existing multi-exposure fusion methods have achieved impressive results, generating high-quality HDR images in dynamic scenes is still difficult. The primary challenges are ghosting artifacts caused by object motion between low dynamic range images and distorted content in under and overexposed regions. In this paper, we propose a deep progressive feature aggregation network for improving HDR imaging quality in dynamic scenes. To address the issues of object motion, our method implicitly samples high-correspondence features and aggregates them in a coarse-to-fine manner for alignment. In addition, our method adopts a densely connected network structure based on the discrete wavelet transform, which aims to decompose the input features into multiple frequency subbands and adaptively restore corrupted contents. Experiments show that our proposed method can achieve state-of-the-art performance under different scenes, compared to other promising HDR imaging methods. Specifically, the HDR images generated by our method contain cleaner and more detailed content, with fewer distortions, leading to better visual quality.

翻译：高动态范围（HDR）成像是图像处理中的一项重要任务，旨在生成不同光照场景下曝光良好的图像。尽管现有的多曝光融合方法已取得令人瞩目的成果，但在动态场景中生成高质量HDR图像仍然具有挑战性。主要难点在于低动态范围图像之间因物体运动产生的鬼影伪影，以及欠曝光和过曝光区域中的内容畸变。本文提出一种深度渐进式特征聚合网络，用于提升动态场景中的HDR成像质量。为应对物体运动问题，我们的方法隐式采样高对应性特征，并以从粗到细的方式进行聚合以实现对齐。此外，该方法采用基于离散小波变换的密集连接网络结构，旨在将输入特征分解为多个频率子带，并自适应地恢复受损内容。实验表明，与其他有前景的HDR成像方法相比，我们的方法在不同场景下均能达到最优性能。具体而言，本方法生成的HDR图像包含更清晰、更丰富的内容，且畸变更少，从而带来更优的视觉质量。