High-quality panchromatic image acquisition method for snapshot hyperspectral imaging Fourier transform spectrometer

Area of Science:

• Spectroscopy
• Image Processing
• Deep Learning

Background:

• High-quality panchromatic images are crucial for pansharpening multi-spectral images.
• Snapshot imaging spectrometers often have low spatial resolution, limiting image quality.

Purpose of the Study:

• To propose a novel method for acquiring high-quality panchromatic images using an aperture-division snapshot imaging spectrometer.
• To achieve simultaneous contrast enhancement and super-resolution for spectral imaging.

Main Methods:

• Utilized a pre-trained deep learning network for image enhancement.
• Leveraged sub-aperture images with shared spatial information for super-resolution.
• Acquired an experimental dataset for network training.

Main Results:

• The proposed method successfully acquired high-quality panchromatic images.
• Demonstrated effective contrast enhancement and super-resolution capabilities.
• Validated performance through experimental results.

Conclusions:

• The developed deep learning approach is effective for high-quality panchromatic image acquisition in snapshot hyperspectral imaging.
• The method offers a solution for improving spatial resolution without instrument redesign.
• This technique benefits applications requiring detailed spectral information.