Single-Shot Transient Imaging Via Compressed Time-Of-Flight Imaging And Dictionary Learning

Abstract

In this work, we propose a single-shot transient imaging framework that combines sparsity-based signal modeling with compressive frequency-domain sampling. The temporal response function of a scene encodes rich information about depth and light transport, and its accurate reconstruction is critical for various imaging applications. To enable transient recovery from a limited number of measurements, we exploit learned sparse representations in an optimized dictionary basis. We compare multiple sampling strategies in the frequency domain and show that both transient profiles and depth maps can be reconstructed under highly compressed acquisition. Notably, we achieve full transient reconstruction using only 16 modulation frequencies, based on real correlation functions, enabling practical singleshot acquisition through spatial frequency multiplexing