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Related Experiment Video

Updated: Jan 11, 2026

Echo Particle Image Velocimetry
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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
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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 networkimage deblurringlidar 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.