Generalizing Imaging Through Scattering Media With Uncertainty Estimates

• Published in: Proceedings (IEEE Winter Conference on Applications of Computer Vision Workshops. Online) pp. 760 - 766
• Main Authors: Cochrane, Jared M., Beveridge, Matthew, Drori, Iddo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2022
• Subjects: Computer architecture; Imaging; Media; Scattering; Speckle; Training; Uncertainty
• ISSN: 2690-621X
• DOI: 10.1109/WACVW54805.2022.00083

Imaging through scattering media is challenging: object features are hidden under highly-scattered photons. Conventional methods that characterize scattering properties, such as the media input-output transmission matrix, are susceptible to environmental disturbance that is not ideal for many imaging scenarios, especially in biomedical imaging. Learning from examples is ideal for imaging in highly scattered regimes because it is adaptable and accurate even when the microstructures of the scattering media change. In current approaches, network output on unseen scattering media contain artifacts that inhibit meaningful object recognition. We present a network architecture that is able to generate high quality images over a range of different scattering media and image sizes with minimal artifacts. Our network learns the statistical information within highly scattered speckle intensity patterns. This allows us to compute an accurate mapping from different speckle patterns to their corresponding objects given scattering media with varying microstructures. Our network demonstrates superior performance compared to similar models, especially when trained on a single scattering medium and then tested on unseen scattering media. We estimate the uncertainty of our approach and use the available data efficiently, increasing the generalizability of predicting objects from unseen scattering media with multiple different diffusers.