Non-systematic noise reduction framework for ToF camera

Time-of-flight (ToF) cameras enable a diverse range of applications due to their high frame rate, high resolution, and low cost. However, these cameras suffer from non-systematic noise during the acquisition of high-quality depth images, severely affecting their range accuracy. In this paper, we propose a non-systematic noise reduction framework named “DCS2Noise” to address this issue. This framework comprises a three-stage denoising strategy, involving noise standardization, deep learning-based differential correlation sampling (DCS) denoising and further enhancement with pairwise noise suppression. This framework directly captures and denoises DCS images during the ToF imaging process, making it more suitable for non-systematic noise reduction in ToF cameras. Compared to traditional methods, our approach significantly reduces the root mean squared error (RMSE) and improves the noise reduction ratio, peak signal to noise ratio (PSNR), and structural similarity index measure (SSIM). We believe that this study provides new insights into understanding noise in ToF cameras and offers effective references for reducing non-systematic noise in three-dimensional measuring instruments.