Efficient Multi-Path Interference Mitigation in Indirect Time-of-Flight Imaging Using Hybrid Optimization

Indirect Time-of-Flight (iToF) technology is widely utilized for precise spatial sensing. However, it is highly suscep-tible to Multi-Path Interference (MPI), which can be induced by semi-transparent objects, sharp edges, or fog and degrade the accuracy of 3D measurement. This study proposes a hybrid optimization approach combining Orthogonal Matching Pursuit (OMP) and Sequential Quadratic Programming (SQP) to effec-tively mitigate MPI in iToF systems. By using OMP to generate initial values for SQP optimization, we demonstrate improved accuracy and faster convergence compared to traditional meth-ods like Particle Swarm Optimization (PSO) combined with the Interior Point Method (IPM). We evaluate the proposed OMP-SQP hybrid method using a publicly available dataset, which includes various challenging scenarios such as stray light interference and closely spaced targets. The experimental results show that the OMP-SQP method achieves up to 3.33 times faster optimization with comparable or even superior accuracy to the existing method. These findings suggest that the OMP-SQP hybrid method is a highly efficient and reliable solution for enhancing the performance of iToF systems under complex MPI conditions.