Selective kernel and motion-emphasized loss based attention-guided network for HDR imaging of dynamic scenes

Yipeng Deng, Qin Liu*, Takeshi Ikenaga

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ghost-like artifact caused by ill-exposed and motion areas is one of the most challenging problems in high dynamic range (HDR) image reconstruction. When the motion range is small, previous methods based on optical flow or patch-match can suppress ghost-like artifacts by first aligning input images before merging them. However, they are not robust enough and still produce artifacts for challenging scenes where large foreground motions exist. To this end, we propose a deep network with an attention module and motion-emphasized loss function to produce ghost-free HDR images. In the attention module, we use the channel and spatial attention to guide the network to emphasize important components such as motion and saturated areas automatically. To be robust to images with different resolutions and objects with distinct scales, we adopt the selective kernel network as the basic framework for channel attention. In addition to the attention module, the motion-emphasized loss function based on the motion and ill-exposed areas mask is designed to help the network reconstruct motion areas. Experiments on the public dataset indicate that the proposed SK-AHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 43.17 with PSNR metric and 61.02 with HDR-VDP-2 metric on test which outperforms all conventional works. According to quantitative and qualitative evaluations, the proposed method can achieve state-of-the-art performance.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8976-8983
Number of pages8
ISBN (Electronic)9781728188089
DOIs
Publication statusPublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: 2021 Jan 102021 Jan 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
Country/TerritoryItaly
CityVirtual, Milan
Period21/1/1021/1/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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