针对鱼眼相机的传统标定过程烦琐并且不适用于日常场景图像的问题，提出了一种新的基于卷积神经网络的方法，可同时标定鱼眼镜头的内参并进行图像畸变校正。该方法通过预测不同畸变参数下像素点的位移量，从而提高鱼眼相机标定和图像畸变校正的精度；为了进一步提高模型精度和泛化性，在编码部分引入坐标注意力模块，增强对图像位置信息的关注度；最后为了增强图像的细节特征，在跨越连接部分设计了跨尺度融合模块。针对数据集稀缺的问题，还生成了一个新的大规模数据集，标有相应的畸变参数和畸变校正后的图像。实验结果表明：与其他鱼眼相机标定方法相比，重投影误差为0.312 pixel，标定的精度较高；与图像畸变处理方法相比，峰值信噪比（peak signal to noise ratio, PSNR）为38.055 dB，结构相似度（structural similarity, SSIM）为0.874，图像畸变校正的质量较好。

To address the cumbersome calibration process of fisheye cameras and its inapplicability to everyday scene images, a novel convolutional neural network(CNN)-based method was proposed that simultaneously calibrates the intrinsic parameters of fisheye lenses and corrects image distortion. The accuracy of fisheye camera calibration and image distortion correction was improved by predicting the displacement of pixel points under different distortion parameters. A coordinate attention module was introduced in the encoding part to enhance the model's accuracy and generalization ability to increase attention to image position information. Additionally, a cross-scale fusion module was designed in the skip connections to enhance image detail features. To address the issues of dataset scarcity and incomplete distortion parameter distribution, a new large-scale dataset labeled with corresponding distortion parameters and images after distortion correction was created. Experimental results show that compared to other fisheye camera calibration methods, this method achieves a reprojection error of 0.312 pixel, indicating the highest calibration accuracy. Additionally, compared to other image distortion correction methods, a peak signal to noise ratio(PSNR) of 38.055 dB and an structural similarity(SSIM) of 0.874 are achieved, indicating the best quality of image distortion correction.