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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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A Multi-Shot Approach for Spatial Resolution Improvement of Multispectral Images from an MSFA Sensor.

Jean Yves Aristide Yao^1,2, Kacoutchy Jean Ayikpa^1,2, Pierre Gouton¹

¹Laboratoire Imagerie et Vision Artificielle (ImVia), Université de Bourgogne, 21000 Dijon, France.

Journal of Imaging

|June 26, 2024

Summary

This summary is machine-generated.

This study introduces a novel sparse image composition method to enhance multispectral imaging. By reducing null pixels and improving spatial resolution, the technique overcomes demosaicing challenges for better image reconstruction.

Keywords:

MSFA one-shot multi-shot system multispectral demosaicing spatial resolution spectral resolution

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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Area of Science:

Optics and Photonics
Image Processing
Computer Vision

Background:

Multispectral imaging utilizes filter arrays for scene acquisition.
Current demosaicing methods struggle with sparse images containing many null pixels.
Challenges include rendering fine details, abrupt transitions, and textured regions.

Purpose of the Study:

To propose a sparse image composition method to improve multispectral image reconstruction.
To address limitations in demosaicing sparse images from multispectral filter arrays.
To enhance spatial resolution and minimize reconstruction errors in multispectral imaging.

Main Methods:

Developed a camera with an eight-band multispectral filter array (MSFA).
Proposed a sparse image composition technique by increasing snapshots with spatial sensor displacement.
Applied a bilinear demosaicing approach to composite sparse images.

Main Results:

The composition method significantly reduces null pixels in sparse images.
Experimental results show improved image spatial resolution.
Reconstruction errors are minimized while preserving spectral fidelity.

Conclusions:

The proposed sparse image composition method effectively enhances multispectral image quality.
This technique overcomes key demosaicing challenges associated with sparse images.
The method offers a viable solution for advanced multispectral imaging applications.