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Updated: Mar 8, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Motion-Adaptive Depth Superresolution.

Ulugbek S Kamilov, Petros T Boufounos

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

|January 28, 2017

Summary

This study enhances depth map resolution by fusing low-resolution depth data with high-resolution video. Exploiting object motion significantly improves depth estimation accuracy for advanced vision systems.

Area of Science:

Computer Vision
Sensor Fusion
Robotics

Background:

Multi-modal sensing is crucial for advanced applications, presenting unique processing challenges.
Combining low-resolution depth sensors with high-resolution optical video offers potential for improved scene understanding.

Purpose of the Study:

To develop a novel method for generating high-resolution depth maps by fusing low-resolution depth and high-resolution video data.
To leverage temporal information and object motion to enhance depth map quality.

Main Methods:

A new formulation integrating temporal information and exploiting object motion.
Utilizing motion-adaptive low-rank regularization to exploit space-time redundancy in depth and intensity data.

Main Results:

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Substantial improvement in the quality of estimated high-resolution depth maps compared to existing methods.
Validation of the proposed approach through experimental results.

Conclusions:

The proposed method effectively generates high-resolution depth maps by combining multi-modal sensor data.
This approach serves as a foundational component for vision systems requiring precise depth information.