Details

Autor(en) / Beteiligte

• Zhang, Wuyang
• Song, Ping
• Bai, Yunjian
• Geng, Haocheng
• Wu, Yinpeng
• Zheng, Zhaolin

Titel

Non-systematic noise reduction framework for ToF camera

Ist Teil von

• Optics and lasers in engineering, 2024-09, Vol.180, p.108324, Article 108324

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •This paper introduces “DCS2Noise,” a novel framework tailored for non-systematic noise reduction in Time-of-Flight (ToF) cameras.•“DCS2Noise” comprises three stages: noise standardization, deep learning-based differential correlation sampling (DCS) denoising, and pairwise noise suppression, offering a comprehensive approach to noise reduction.•“DCS2Noise” directly captures and denoises DCS images during the ToF imaging process, enhancing its suitability for addressing non-systematic noise in ToF cameras.•This study provides valuable insights into understanding noise in ToF cameras, offering effective references for reducing non-systematic noise in three-dimensional measuring instruments. 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.