Deep Learning-Based Predictive Analysis of Daylight Transitions in Photographic Images

Accurately simulating daylight transitions in photo- graphic pictures is highly valuable for a variety of applications, from computer vision and environmental modeling to virtual photography and post-production. This work introduces a novel method for simulating and predicting morning, afternoon, and nighttime lighting conditions from a single input photograph using a deep learning-based Generative Adversarial Network (GAN). The suggested method uses a PatchGAN discriminator in conjunction with a U-Net generator architecture that was trained on a carefully selected dataset of outdoor sceneries in various lighting scenarios. With the use of quantitative measurements (PSNR, SSIM, LPIPS) and qualitative visual evaluations, we show how effective our method is at producing realistic and temporally consistent illumination changes. We also talk about the shortcomings of the existing strategy and offer potential directions for further study.Advanced algorithms based on ar- tificial intelligence are used in computational photography to enhance image quality. Current iPhones improve illumination, dynamic range, and quality by combining many pictures into a single final image rather of depending only on sensors and lenses. For instance, this technique makes images captured in low light appear as though they were captured by professional photographers.