SFRC-Flow: Spatial–Frequency Dual-Domain Refinement and Motion Prior Calibration for Low-Light Optical Flow Estimation

Abstract

Optical flow estimation is a fundamental task in computer vision and visual sensing systems. However, most existing approaches are designed for normal illumination conditions. In low-light scenarios, inherent imaging noise and low contrast lead to noticeable feature degradation and matching ambiguity, which compromise estimation accuracy. To address these issues, this article proposes spatial-frequency dual-domain refinement and motion prior calibration (SFRC)-Flow, a robust low-light optical flow estimation method that integrates spatial–frequency dual-domain feature refinement and motion prior calibration to ensure reliable feature learning and alleviate matching ambiguity. Specifically, the dual-domain refinement encoder (DDRE) first decomposes shallow spatial features into high-frequency local details and low-frequency global structures. Subsequently, we propose the global–local feature alignment module (GLFAM) to resolve spatial and semantic inconsistencies between these decomposed features across different branches via cross-branch feature alignment. Building upon this, we further introduce a cascaded three-stage frequency-domain refinement module (FDRM) to compensate for the limited long-range modeling capability of spatial-only operations and recover degraded motion features. Finally, we present the motion prior-aware calibration module (MPACM) to incorporate motion cues into window-constrained semantic attention. This module produces motion vectors as prior knowledge to calibrate the subsequent flow regression process. Extensive experiments on the flying chairs-dark noise (FCDN), various brightness optical flow (VBOF), and teledyne forward looking infrared advanced driver assistance systems thermal dataset (FLIR ADAS) datasets show that SFRC-Flow achieves competitive accuracy for low-light optical flow estimation.