UC Berkeley

Most imaging systems were developed to capture images for static objects that do not move during the image acquisition time. As a result, motion is considered as a primary source of imaging artifacts, which limits the observation of fast moving samples. The most common way to suppress motion artifacts is to shorten the acquisition time, thereby minimizing motion during the observation. However, this comes with a cost of less signal and increased noise in the measurements. While most imaging systems assume static scenes and well-calibrated system motion during image acquisition, this thesis pioneers an alternative approach that algorithmically estimates unknown motion. With accurate motion estimation, we can computationally correct motion artifacts during the image reconstruction process, opening opportunities to design imaging systems specifically for dynamic scenes. The core idea of this thesis is to simultaneously reconstruct both the object and its motion using optimization techniques. We find this approach to be effective and versatile, demonstrating it across various imaging modalities, object scales, and applications. This joint object and motion optimization can be done in post-processing without altering image acquisition, enabling the reconstruction of dynamic scenes from existing datasets affected by motion artifacts.

In addition to recovering dynamic information from static imaging systems, we also explore an opposite problem: recovering static scenes using an event camera that only detects changes in the scene. By studying the triggering mechanism of noise events, we develop a statistical noise model for the event camera that explains its illuminance-dependent noise characteristics. With this understanding, we propose to form an image of a static scene using only noise events, providing rich contextual information about static scenes to the dynamic sensor, without requiring any change on its hardware.

This thesis demonstrates these concepts using various novel imaging systems, including an event camera, a lensless camera, a quantitative phase microscope, a 3D refractive-index microscope, and a super-resolution fluorescence microscope. By accurately modeling both unknown motion and noise, we aim to demonstrate how computational methods can bridge the gap between static and dynamic imaging.