Enhanced Night-to-Day Image Conversion Using CycleGAN-Based Base-Detail Paired Training

Numerous studies are underway to enhance the identification of surroundings in nighttime environments. These studies explore methods such as utilizing infrared images to improve night image visibility or converting night images into day-like representations for enhanced visibility. This research presents a technique focused on converting the road conditions depicted in night images to resemble daytime scenes. To facilitate this, a paired dataset is created by augmenting limited day and night image data using CycleGAN. The model is trained using both original night images and single-scale luminance transform (SLAT) day images to enhance the level of detail in the converted daytime images. However, the generated daytime images may exhibit sharpness and noise issues. To address these concerns, an image processing approach, inspired by the Stevens effect and local blurring, which align with visual characteristics, is employed to reduce noise and enhance image details. Consequently, this study contributes to improving the visibility of night images by means of day image conversion and subsequent image processing. The proposed night-to-day image translation in this study has a processing time of 0.81 s, including image processing, which is less than one second. Therefore, it is considered valuable as a module for daytime image translation. Additionally, the image quality assessment metric, BRISQUE, yielded a score of 19.8, indicating better performance compared to conventional methods. The outcomes of this research hold potential applications in fields such as CCTV surveillance systems and self-driving cars.