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Light Acquisition02:16

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Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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End-to-End Depth-Guided Relighting Using Lightweight Deep Learning-Based Method.

Sabari Nathan1, Priya Kansal1

  • 1Couger Inc., Tokyo 150-0001, Japan.

Journal of Imaging
|September 27, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a new deep learning model for image relighting, using depth information to adjust lighting while keeping content intact. The model effectively handles diverse lighting conditions, producing realistic results.

Keywords:
depth-guidedimage enhancementimage relighting

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Area of Science:

  • Computer Vision
  • Deep Learning

Background:

  • Image relighting is crucial for altering lighting conditions while preserving visual content.
  • Existing methods may struggle with complex lighting variations.

Purpose of the Study:

  • To introduce a bi-modal lightweight deep learning model for depth-guided image relighting.
  • To enhance feature representation for improved relighting accuracy.

Main Methods:

  • Utilized a Res2Net Squeezed block for capturing long-range dependencies and enhancing feature representation.
  • Employed an encoder-decoder architecture with Res2Net Squeezed blocks.
  • Trained and evaluated on the VIDIT dataset (300 image triplets).

Main Results:

  • The model effectively handles complex lighting variations, including different illuminant angles and color temperatures.
  • Achieved high relighting accuracy, validated by PSNR, SSIM, and visual quality metrics.
  • Demonstrated improved information flow for realistic relit image generation.

Conclusions:

  • The proposed depth-guided relighting model is effective and efficient.
  • The Res2Net Squeezed blocks significantly contribute to handling complex lighting.
  • The approach generates realistic relit images with high fidelity.