A Convolutional Neural Network for Small Sample's Ring Structured Light Denoising

Denoising is an indispensable step when measuring depth by using omnidirectional ring structured light depth perception system. However, it is difficult to obtain uniform, clear and continuous structured light stripe by using general image denoising algorithm. To solve this problem, our paper adopts the method of combining Deep Convolutional Generative Adversarial Networks (DCGAN) and Denoising Convolutional Neural Network-Ring Structured Light (DnCNN-RSL) to denoise. We first use DCGAN network to generate more data sets, and then use DnCNN-RSL network to denoise images so that noiseless images can be obtained. DnCNN-RSL network is suitable for removing the noise of small sample data sets, which reflects in improving PNSR value of images, and SSIM value is also closer to 1. Experiments show that DnCNN-RSL network can get better denoising results than traditional image processing methods and obtain clear, continuous structured light stripe. In this way depth information obtained by denoised structured light stripe is more accurate.