Integral imaging based on sparse camera array and CNN super-resolution

Abstract

The super-resolution integrated imaging based on sparse camera array and convolution neural network can reduce the rendering time by reducing the number of cameras, and then reconstruct the low-resolution element image into highresolution element image by using convolutional neural network. In order to further improve the effect of element image reconstruction, this paper improves the network model optimizer and sensitive parameters, constructs activation function and loss function, and uses smaller convolution kernel in the last layer of convolution neural network to improve the quality of the generated element image. At last, the original scheme and the improved scheme are verified and compared through the TensorFlow platform. The experimental results show that the reconstruction element image generated by the improved scheme is better and the network training time is shorter.