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High dynamic range (HDR) imaging is an important task in image processingthat aims to generate well-exposed images in scenes with varying illumination.Although existing multi-exposure fusion methods have achieved impressiveresults, generating high-quality HDR images in dynamic scenes is stilldifficult. The primary challenges are ghosting artifacts caused by objectmotion between low dynamic range images and distorted content in under andoverexposed regions. In this paper, we propose a deep progressive featureaggregation network for improving HDR imaging quality in dynamic scenes. Toaddress the issues of object motion, our method implicitly sampleshigh-correspondence features and aggregates them in a coarse-to-fine manner foralignment. In addition, our method adopts a densely connected network structurebased on the discrete wavelet transform, which aims to decompose the inputfeatures into multiple frequency subbands and adaptively restore corruptedcontents. Experiments show that our proposed method can achievestate-of-the-art performance under different scenes, compared to otherpromising HDR imaging methods. Specifically, the HDR images generated by ourmethod contain cleaner and more detailed content, with fewer distortions,leading to better visual quality.

Deep Progressive Feature Aggregation Network for High Dynamic Range Imaging