Abstract: In order to improve the final image quality of a large field single-lens computational imaging system, and to make its output more suitable for human eyes to see, a feasible image training method is proposed in this paper. First, a image was divided into two parts, including the center and edge areas, according to the field of view. In order to avoid leaving segmentation traces after splicing, we adopted a segmentation method that can leave a Gaussian boundary. Then the two parts were put into two datasets respectively. After that, the two datasets were respectively fed into the neural network for training. After training, the test image was divided into the center and edge areas using the same method, and were fed into their own neural networks. Finally, the results of the two networks would be joined together into a complete image to get the final result. After subjective perception and objective index evaluation, the image obtained by using the new idea in this paper has a significant improvement in quality and a better visual perception compared with the image obtained by direct training. Therefore, the improvement and optimization of the large field of view single-lens computational imaging system is successfully realized, and the output images of the system become more suitable for human eyes.