25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 15 - 18 May 2017
In our ever changing world, natural outdoor scenes undergo significant changes due to lighting, weather and seasonal conditions at different times of the day and the year. Therefore, it. is remarkably challenging to build computational models which can automatically manipulate the appearance of outdoor images in a realistic manner. Suggested approaches employ several intermediate steps that may seriously affect the quality of the result, such as retrieving similar images in a large database and matching those images to the input image. As an effort to eliminate these drawbacks of the previous methods, in this paper, we present an automatic image editing approach which utilizes generative adversarial networks to learn the appearance manifold of outdoor images. Our experiments show that our model yields natural looking promising results.