earticle

영어

High-resolution image data taken by drones is being used as spatial information in various industries such as building maintenance, facility diagnosis, and urban planning modeling. In the industrial field, it is easy to visualize 3D on the web without installing separate software, and it is easy to run on various devices such as desktops and tablets and tablets regardless of platform environment, so it is used for visualization of drone high-resolution image data. However, in the process of processing high-resolution image data on a web browser, it has structural limitations such as rendering limitations, resampling, and downscaling, which degrade the actual image quality. This study proposes a method to restore drone high-resolution image quality using SRCNN (Super Resolution Convolutional Neural Network) so that the degraded image quality can be expressed at the original level on a web browser. We compared the restoration performance of applying SRCNN in a local environment for some of the 91 images taken by a drone at the Industry-Academic Cooperation Center building of *** University in Busan, Korea. The results show that SRCNN can be applied to restore image quality. In this study, a possible method for improving the image quality degradation problem in web browsers using AI-based post-processing was presented, and it is expected to contribute to web visualization and platform implementation of large amounts of high-resolution image data in the future.