Abstract
Fog obscuring images is a severe problem in observing a city using observation cameras installed at high altitudes, such as at the top of a mountain. In recent years, research has been conducted on Dehazing, which removes fog from an image and converts it into a clear image. However, most of these methods involve fog processing on clear images and transforming them by having a machine learning model learn image pairs with perfectly matched angles of view. It is difficult to collect clear and foggy image pairs with matched angles of view from cameras installed at high elevations due to angle-of-view control and wind effects. In addition, the actual fog is different from the images obtained by fog processing, making it difficult to apply the methods of previous studies to the city observation task. Therefore, this paper uses CycleGAN, which learns a set of sunny day images and a set of fog images and performs domain transformation. Also, images actually taken were used for the training images. As a result, we were able to remove some of the fog in the images captured by the observation camera. In the stitching task between the captured images, the fog images could not be stitched together but could be stitched together after the fog removal process.
