WaveMo: Learning Wavefront Modulations to See Through Scattering
Mingyang Xie Haiyun Guo Brandon Y. Feng Lingbo Jin Ashok Veeraraghavan Christopher A. Metzler
Imaging through scattering media is a fundamental and pervasive challenge in
fields ranging from medical diagnostics to astronomy. A promising strategy to
overcome this challenge is wavefront modulation, which induces measurement
diversity during image acquisition. Despite its importance, designing optimal
wavefront modulations to image through scattering remains under-explored. This
paper introduces a novel learning-based framework to address the gap. Our
approach jointly optimizes wavefront modulations and a computationally
lightweight feedforward "proxy" reconstruction network. This network is trained
to recover scenes obscured by scattering, using measurements that are modified
by these modulations. The learned modulations produced by our framework
generalize effectively to unseen scattering scenarios and exhibit remarkable
versatility. During deployment, the learned modulations can be decoupled from
the proxy network to augment other more computationally expensive restoration
algorithms. Through extensive experiments, we demonstrate our approach
significantly advances the state of the art in imaging through scattering
media. Our project webpage is at https://wavemo-2024.github.io/.