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Related Experiment Video

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

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Published on: December 15, 2023

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

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-guided image enhancement image 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.