论文标题
高动态范围成像的监督图像分割
Supervised Image Segmentation for High Dynamic Range Imaging
论文作者
论文摘要
常规摄像机和手机能够捕获有限的光度。因此，就质量而言，大多数来自此类设备的图像与现实世界不同。它们过于黑暗或太亮，细节并不完美。可以使用各种以高动态范围（HDR）成像为名的方法来解决此问题。他们的目标是制作更多细节的图像。但是，不幸的是，从多曝光图像中生成HDR图像的大多数方法仅集中于如何结合不同的曝光，并且不关注选择每个图像的最佳细节。因此，在这项研究中，它在图像分割的帮助下提取每个图像的最明显区域。考虑了手动阈值和OTSU阈值，考虑了两种产生地面真理的方法，并且将使用神经网络来训练这些区域。最后，可以证明神经网络能够接受图片的可见部分。
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.
