Multi-level Feature Aggregation Network for High Dynamic Range Imaging

Modern digital cameras typically cannot capture the whole range of illumination, due to the limited sensing capability of sensor devices. High dynamic range (HDR) imaging aims to generate images with a larger range of illumination by merging multiple low-dynamic range (LDR) images with different exposure times. However, when the images are captured in dynamic scenes, existing methods unavoidably produce undesirable artifacts and distorted content. In this paper, we propose a multi-level feature aggregation network, based on the Laplacian pyramid, to address this issue for HDR imaging. The proposed method progressively aggregates non-overlapping frequency sub-bands at different pyramid levels, and generates the corresponding HDR image from coarser to finer scales. Experiment results show that our proposed method can significantly outperform other competitive HDR methods, thereby producing HDR images with high visual quality.