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Related Experiment Video

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Normalizing images in various weather and lighting conditions using ColorPix2Pix generative adversarial network.

Sanjida Tasnim¹, Ashif Mahmud Mostafa¹, Azmain Morshed¹

¹Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.

Scientific Reports

|September 30, 2025

Summary

This study introduces ColorPix2Pix, an advanced Generative Adversarial Network (GAN) algorithm, to improve image normalization for autonomous vehicles. The novel GAN enhances perception system reliability in adverse lighting and weather conditions.

Keywords:

Color vision Generative adversarial networks Image normalization Road scene image enhancement

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Autonomous vehicles (AVs) rely on accurate perception systems for safe operation.
Deep learning object detection enhances AV perception but remains vulnerable to environmental variations like poor lighting and adverse weather.
Current perception systems struggle with reliability and safety due to environmental factors affecting image quality.

Purpose of the Study:

To develop an advanced color vision technique for normalizing images captured in hazardous environmental and lighting conditions.
To introduce an efficient algorithm, ColorPix2Pix, based on optimized Generative Adversarial Network (GAN) models.
To enhance the reliability and safety of autonomous vehicle perception systems under diverse environmental challenges.

Main Methods:

Proposed a novel ColorPix2Pix Generative Adversarial Network (GAN) model with an enhanced loss function prioritizing structural and color fidelity.
Employed a two-phase training process using comprehensive datasets simulating fog, rain, and variable illumination.
Utilized a custom loss function combining perceptual loss and color consistency measures for noise reduction and detail restoration.

Main Results:

The ColorPix2Pix GAN effectively normalized images affected by extreme lighting and weather conditions.
Achieved high performance metrics on lighting datasets (SSIM: 0.767, PSNR: 68.581) and weather datasets (SSIM: 0.660, PSNR: 67.185).
Demonstrated superior performance over existing image normalization methods in restoring image quality and perceptual reliability.

Conclusions:

The ColorPix2Pix algorithm significantly improves image normalization for autonomous vehicle perception systems.
The proposed GAN model enhances robustness against adverse environmental conditions, crucial for AV safety.
This research contributes to more reliable and safer autonomous driving through advanced image processing techniques.