Monte Carlo Noise Reduction Algorithm Based on Deep Neural Network in Efficient Indoor Scene Rendering System

Because of its flexibility and universality, Monte Carlo integral has become the preferred algorithm of most realistic image synthesis. However, the quality of rendered images is often affected by the estimated variance, which is mainly reflected in image noise visually. To reduce the variance, Monte Carlo rendering systems often require extensive sampling, which also causes a lot of time spent trying to render noiseless images. For this problem, we propose a Monte Carlo noise reduction algorithm based on deep neural networks and apply it to the efficient rendering of an indoor scene. The algorithm can reduce noise in real time. In order to solve the gradient disappearance problem of deep convolutional neural network, the residual structure of the network is added to the original convolutional network. The proposed algorithm can achieve better noise reduction quality in the real-time guarantee.