Supervised image segmentation for high dynamic range imaging

Regular cameras and cell phones are able to capture limited luminosity. In terms of quality, most of the produced images by such devices are not similar to the real world. Various methods, which fall under the name of High Dynamic Range (HDR) Imaging, can be utilised to cope with this problem and produce an image with more details. However, most methods for generating an HDR image from Multi-Exposure images only focus on how to combine different exposures and do not consider the choice the best details of each image. By convers, in this research it is strived to detect the most visible areas of each image with the help of image segmentation. Two methods of producing the Ground Truth are considered, as manual and Otsu thresholding, and two similar neural networks are used to train segment these areas. Finally, it is shown that the neural network is able to segment the visible parts of pictures acceptably.