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Related Experiment Video

Updated: Jan 11, 2026

Echo Particle Image Velocimetry

Echo Particle Image Velocimetry

Published on: December 27, 2012

Deep Lidar-Guided Image Deblurring.

Ziyao Yi¹, Diego Valsesia¹, Tiziano Bianchi¹

¹Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This summary is machine-generated.

This study shows that depth data from smartphone Lidar sensors significantly improves image deblurring. Integrating this depth information enhances neural deblurring models, boosting performance with minimal parameter increase.

Keywords:

deep neural network image deblurring lidar depth map

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Area of Science:

Computer Vision
Image Processing
Sensor Technology

Background:

Portable Lidar instruments offer new possibilities for depth-assisted image processing.
Recent smartphones equipped with Lidar sensors provide depth information.

Purpose of the Study:

To investigate the utility of mobile Lidar depth data for image deblurring.
To develop a general method for integrating depth information into neural deblurring models.

Main Methods:

A continual learning strategy using adapters within U-shaped encoder-decoder models was developed.
Depth information was efficiently preprocessed to modulate image features.

Main Results:

The proposed method consistently improved performance across various state-of-the-art deblurring baselines.
Peak Signal-to-Noise Ratio (PSNR) gains of up to 2.1 dB were achieved.
A modest increase in model parameters was observed.

Conclusions:

True depth information from smartphone Lidar significantly enhances image deblurring effectiveness.
The encoder-decoder architecture effectively integrates depth features for improved deblurring.