Year: 2024
Type: conference-paper
Abstract: Extreme weather conditions may hinder predicting object detection through the camera sensors. Snow, dust, and rain particles can cause degraded areas and obstruct the camera vision, even if a human being is still recognizable. To eliminate the hindering particles, we apply the denoising techniques to restore the images without any weather-affected. However, the images may require the proper neural network training, and the hindering particles must be recognized. In this paper, we propose paired-data transformations to the pix2pix architecture to recognize the hindering particle effects. After applying paired-data transformations, the structural similarity index measure (SSIM) surpassed 92%, and the percentage showed 16.21% improvement.
Authors: Jung Hwan Kim, Dong Seog Han
Institution: Kyungpook National University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415168029