Image Visibility Enhancement Under Inclement Weather with an Intensified Generative Training Set

Image-to-image translation inputs an image and transforms it into a new image. Deep learning-based image translation requires numerous training data to prevent overfitting; therefore, this study proposes a method to secure training data efficiently by generating and selecting fake water-droplet images using a cycle-consistent generative adversarial network (CycleGAN) and a convolutional neural network (CNN) for image enhancement under inclement weather conditions. A CNN-based classification model was employed to select 1200 well-formed virtual paired sets, which were then added to the existing dataset to construct an augmented training set. Using this augmented dataset, a CycleGAN-based removal module was trained with a modified L1 loss incorporating a difference map, enabling the model to focus on water-droplet regions while preserving the background color configuration. Additionally, we introduce a second training step with tone-mapped target images based on Retinex theory and CLAHE to enhance image contrast and detail preservation under low-light rainy conditions. Experimental results demonstrate that the proposed framework improves water-droplet removal performance compared to the baseline, achieving higher scores in image quality metrics such as BRISQUE and SSEQ and yielding clearer images with reduced color distortion. These findings indicate that the proposed approach contributes to improving image clarity and the safety of autonomous driving under inclement weather conditions.