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Updated: May 10, 2025

A Human Cerebral Organoid Model of Neural Cell Transplantation

A Human Cerebral Organoid Model of Neural Cell Transplantation

Published on: July 21, 2023

Local Binary Pattern-Cycle Generative Adversarial Network Transfer: Transforming Image Style from Day to Night.

Abeer Almohamade^1,2, Salma Kammoun¹, Fawaz Alsolami¹

¹Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Journal of Imaging

|April 25, 2025

Summary

This summary is machine-generated.

LBP-CycleGAN enhances night-time image translation by using Local Binary Patterns (LBP) for sharper textures. The model without self-attention achieved superior quality and reduced computational costs for applications like autonomous driving.

Keywords:

Local Binary Pattern (LBP)cycle generative adversarial network (CycleGAN)transform image style unpaired image-to-image translation

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Image-to-image translation is vital for autonomous driving and surveillance.
Existing CycleGAN models face challenges like texture loss and high computational demands.

Purpose of the Study:

To develop an improved CycleGAN model for day-to-night image translation.
To address texture loss and computational inefficiency in existing methods.

Main Methods:

Introduced LBP-CycleGAN, utilizing Local Binary Patterns (LBP) for texture detail extraction.
Leveraged LBP-based single-channel inputs for improved night-time image generation.
Evaluated three variations: LBP-CycleGAN with self-attention (full, discriminator-only), and without self-attention.

Main Results:

The LBP-CycleGAN model without self-attention yielded superior texture quality compared to other variations.
This model significantly reduced training time and computational overhead.
Demonstrated enhanced sharpness and consistency in night-time textures.

Conclusions:

LBP-CycleGAN offers an efficient solution for high-fidelity night-time image translation.
The simplified model (without self-attention) provides optimal performance.
This advancement benefits real-world applications such as autonomous driving and low-light vision systems.