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.

翻译：常规照相机和手机能够捕捉有限的光度,因此,从质量上看,这些装置产生的图像大多与真实世界不相近,它们太暗或太亮,细节不完全可见。可以使用以高动态区域成像(HDR)为名的各种方法来解决这个问题。它们的目标是用更多细节来制作图像。但不幸的是,从多曝光图像中生成《人类发展报告》图像的大多数方法都集中在如何结合不同曝光,而并不专注于选择每个图像的最佳细节。因此,在这项研究中,它努力利用图像分割来提取每个图像中最可见的区域。人们认为,制作地面真理的两种方法是手动阈值和Otsu的阈值,而神经网络将用来对这些地区进行部分培训。最后,将表明神经网络能够将可见的图像部分进行可接受的分割。