Multiprojector Interreflection Compensation Using a Deep Convolution Network

Abstract

The aim of multiprojector interreflection compensation is to modify input images to remove complex physical stray-light effects (interreflection) from a multiprojector immersive system. This is an important but often ignored problem, which can lead to degradation of a projection image. Traditional methods usually address this problem by computing a matrix inversion. These traditional methods often ignore issue of the clarity of the generated images. In this paper, we describe a method for learning the inversion using a deep convolutional neural network (CNN), named Superresolution Compensation Net (SRCN). SRCN consists of four convolution layers to learn interactions of global light, six convolution layers, and two transposed convolution layers to extract multilevel features and generate compensation images. We also used a subpixel convolution layer to increase the resolution. To make compensation images more consistent with human visual perception, we used a perceptual loss, which compares the differences between feature maps on the VGG16 network. We implemented an immersive projector-camera display prototype (Pro-Cam) and calculated the quality index of the compensation images and the projection results. Our method achieved better results than previous methods in both objective evaluations and subjective visual perception.