Deep unfolding Network for Hyperspectral Image Super-Resolution with
  Automatic Exposure Correction
Yuan Fang Yipeng Liu Jie Chen Zhen Long Ao Li Chong-Yung Chi Ce Zhu
In recent years, the fusion of high spatial resolution multispectral image
(HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been
recognized as an effective method for HSI super-resolution (HSI-SR). However,
both HSI and MSI may be acquired under extreme conditions such as night or
poorly illuminating scenarios, which may cause different exposure levels,
thereby seriously downgrading the yielded HSISR. In contrast to most existing
methods based on respective low-light enhancements (LLIE) of MSI and HSI
followed by their fusion, a deep Unfolding HSI Super-Resolution with Automatic
Exposure Correction (UHSR-AEC) is proposed, that can effectively generate a
high-quality fused HSI-SR (in texture and features) even under very imbalanced
exposures, thanks to the correlation between LLIE and HSI-SR taken into
account. Extensive experiments are provided to demonstrate the state-of-the-art
overall performance of the proposed UHSR-AEC, including comparison with some
benchmark peer methods.