Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
This addresses the problem of degraded image quality in remote sensing and imaging applications due to imbalanced exposures, offering an integrated solution rather than incremental improvements.
The paper tackles hyperspectral image super-resolution under extreme exposure conditions by proposing a deep unfolding network that integrates automatic exposure correction, achieving state-of-the-art performance in generating high-quality fused images with improved texture and features.
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.