Motion Adaptive Deblurring with Single-Photon Cameras

TL;DR

This paper presents a novel motion deblurring algorithm for low-light imaging using SPAD cameras, leveraging their photon detection data to adaptively reduce motion blur in various scenarios.

Contribution

It introduces a method that estimates pixel motion from photon timestamps and dynamically adjusts integration windows to minimize blur, a novel approach for SPAD-based imaging.

Findings

Effective motion blur reduction demonstrated in simulations.

Successful real-world application on a 32x32 SPAD camera.

Applicable to diverse motion profiles including translation and rotation.

Abstract

Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme low-light sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work we harness these capabilities for dealing with motion blur in a passive imaging setting in low illumination conditions. Our key insight is that the data captured by a SPAD array camera can be represented as a 3D spatio-temporal tensor of photon detection events which can be integrated along arbitrary spatio-temporal trajectories with dynamically varying integration windows, depending on scene motion. We propose an algorithm that estimates pixel motion from photon timestamp data and dynamically adapts the integration windows to minimize motion blur. Our simulation results show the applicability of this algorithm to a variety of motion profiles including translation, rotation and local object motion. We also demonstrate the real-world feasibility of our method on data captured using a 32x32 SPAD camera.