大视场单镜片计算成像系统图像分割学习方法

Image segmentation learning method for large field single lens computational imaging system

为了提升大视场单镜片计算成像系统的最终成像质量,本文提出了一种可行的图像训练思路和方法。首先将图像按照视场环切成中心和边缘两部分,然后将两部分分别做成两个数据集并分别训练两个数据集,之后用同样的分割方法将测试图像分成中心和边缘两部分并将其输入对应的网络,最后将两个网络输出结果拼接成完整的图片得到最终结果。通过主观观感和客观指标评价后,使用本文新思路得到的图像比直接训练得到的图像有明显的质量提升,成功实现了对大视场单镜片计算成像系统的改进和优化。

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.