  Regular cameras and cell phones are able to capture limited luminosity. Thus,
in terms of quality, most of the produced images from such devices are not
similar to the real world. They are overly dark or too bright, and the details
are not perfectly visible. Various methods, which fall under the name of High
Dynamic Range (HDR) Imaging, can be utilised to cope with this problem. Their
objective is to produce an image with more details. However, unfortunately,
most methods for generating an HDR image from Multi-Exposure images only
concentrate on how to combine different exposures and do not have any focus on
choosing the best details of each image. Therefore, it is strived in this
research to extract the most visible areas of each image with the help of image
segmentation. Two methods of producing the Ground Truth were considered, as
manual threshold and Otsu threshold, and a neural network will be used to train
segment these areas. Finally, it will be shown that the neural network is able
to segment the visible parts of pictures acceptably.
