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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Joint MAP registration and high-resolution image estimation using a sequence of undersampled images.

R C Hardie¹, K J Barnard, E E Armstrong

¹Dept. of Electr. and Comput. Eng., Dayton Univ., OH.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1997

Summary

This study introduces a novel Maximum A Posteriori (MAP) framework to enhance image resolution and reduce aliasing from undersampled frames. The method jointly estimates registration parameters and the high-resolution image for improved imaging quality.

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Area of Science:

Image processing
Computational imaging
Signal processing

Background:

Detector arrays in imaging systems often lack sufficient density, leading to aliased images, especially in infrared focal plane arrays.
Previous methods for super-resolution from undersampled frames often require known registration parameters or use unsuitable techniques for aliased images.

Purpose of the Study:

To present a Maximum A Posteriori (MAP) framework for estimating high-resolution images with reduced aliasing from a sequence of undersampled frames.
To jointly estimate image registration parameters and the high-resolution image within this MAP framework.

Main Methods:

A cyclic coordinate-descent optimization procedure is employed to iteratively update registration parameters and the high-resolution image.
The proposed method addresses severely aliased images by integrating registration parameter estimation into the super-resolution process.

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Last Updated: Jul 7, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Main Results:

Experimental results demonstrate the effectiveness of the proposed MAP algorithm in improving image resolution and reducing aliasing.
Performance is validated using both visible and infrared imaging data, with quantitative error analysis and subjective evaluations provided.

Conclusions:

The developed MAP framework offers a robust solution for super-resolution imaging from undersampled sequences, particularly in scenarios with severe aliasing.
Jointly estimating registration parameters and the high-resolution image within the MAP framework leads to superior performance compared to prior approaches.