  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.
