AMCW Depth Image Motion Blur Deblurring by DCS Alignment Using Optical Flow
Jiho Ryoo, Soohee Han · 2025
The widespread adoption of depth cameras has been driven by their cost-effectiveness and depth measurement reliability across diverse applications. These sensors, however, face common challenges inherent to optical systems, particularly motion blur artifacts when deployed on moving platforms. This paper introduces a novel end-to-end approach for motion blur removal in Amplitude Modulated Continuous Wave (AMCW) depth cameras, specifically designed to handle High Dynamic Range (HDR) depth imagery. Our method leverages an optical flow network trained on Differential Correlation Sample (DCS) images to achieve proper alignment prior to phase unwrapping. We present a new real-HDR DCS dataset and develop an unsupervised training framework utilizing custom loss functions with adaptive scheduling, eliminating the need for ground truth data. While our implementation did not achieve fully successful optical flow generation for phase unwrapping, our experiments demonstrate the potential viability of optical flow-based approaches for depth image deblurring.