A Distorted Image Correction Method Based on Neural Networks
一种基于神经网络的畸变图像校正方法

Images with geometrical distortions, which are taken by cameras, must be corrected before being analyzed. According to the normal distortion correction method for distorted images which obtains distortion coefficients by setting up a distortion model, but as the calculation is complicated and numerical error becomes a big problem, a distortion correction method based on neural networks is put forward in this paper. First of all, the sample coordinates which serve as input parameters of neural networks are extracted from a distorted template image by image processing technique. Then the neural networks are trained by samples. The trained neural networks can learn any distortion relationship between the normal image and the distorted image. Experiments are done by the new method in this paper, and the correcting results are given and analyzed. The experimental results show the neural networks distortion correction technique is satisfactory and it is used in the vision system of the welding robot.