A fisheye image correction method based on deep learning

Purpose Fisheye camera is a wide-angle camera with a large field of view, which can obtain more information than conventional cameras, but its own disadvantage of large distortion can lead to shape distortion of objects in the captured image. To correct lens distortion, most scholars use manual calibration methods. The traditional manual calibration method relies on complex data computation and specialized mathematical knowledge, but the method is complex and not universal. Considering the nonlinear features of neural networks can be used to fit the nonlinear distortion of distorted images, this paper aims to correct the distortion of a fisheye image based on convolutional neural networks. Design/methodology/approach By generating fisheye distortion images using readily available cityscape road data sets, this paper designs an effective correction model by taking advantage of the fact that the relationship between the pixel coordinates is basically stable after uniform distortion. In our training process, the corresponding distortion is firstly synthesized using the original image, the loss function is constructed using the geometric means of the Hough transform, the whole model is then trained with the help of the loss of the linear slope, and finally the predicted parameters are used to correct the fisheye image, which realizes the overall end-to-end framework. Findings Experimental results show that the method proposed in this paper outperforms similar methods in terms of correction performance. Originality/value This paper proposed a fisheye distortion correction algorithm based on convolutional neural network, which uses distortion-free images based on a generalized fisheye imaging model and maps to generate a fisheye distortion picture data set. It also builds a loss function training model based on line reconstruction error loss of Hough transforms.