Paired-Data Tranfromations for Weather-Affected Images

초록

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. © 2024 Copyright held by the owner/author(s).