| Title |
Development of 2D Image Distortion Classification and Image Angle Calibration System Based on Deep Learning |
| Authors |
김선화(SeonHwa Kim) ; 김유진(YuJin Kim) ; 최수민(SuMin Choi) ; 서창진(ChangJin Seo) |
| DOI |
https://doi.org/10.5370/KIEEP.2022.71.3.190 |
| Keywords |
Neural Network; Image Processing; Image Distortion; Camera Calibration |
| Abstract |
When taking a picture with a camera, the distortion that is different from reality occurs due to wide-angle lenses and camera angles.
In this paper, we propose an image distortion classification and calibration program that provides users with standard images before distortion by classification and calibrating distortion. The program automatically predicts camera parameters from a single input image and proceeds with calibration. Inputting the image, distortion image classification using deep learning (CNN) determines whether Wide-angle lens distortion and Camera-angle distortion exist. When it is determined that distortion exists, deep learning and OpenCV are used to calibrate the distortion state according to each image characteristic. As a result of the program operation, it was confirmed that the output image was calibrated similarly to the actual one, and more fine distortion calibration can be expected by finding distortions that were difficult to judge only with human eyes. |