PhD Proposal: Computational Photography in Challenging Conditions via Physical Cues and Generative Priors

Computational photography uses computer algorithms to recover clean visual images or videos from degraded inputs. This process becomes particularly challenging in real-world environments, such as seeing through scattering media (e.g., fog) or reflective surfaces (e.g., glass), where complex light transport corrupts camera measurements. Most existing methods attempt to learn a direct mapping from degraded inputs to clean outputs, but such problems are often severely ill-posed and result in limited performance.
This proposal focuses on two complementary strategies: (1) introducing physical cues, such as active illumination or optical modulation, to improve fidelity and reduce ambiguity; and (2) incorporating generative priors to plausibly complete missing details when physical cues alone are insufficient.
To demonstrate these strategies, this proposal presents 3 works of mine: WaveMo learns to modulate light wavefronts for seeing through scattering media. Flash-Splat performs 3D reflection removal by combining flash cues with 3D Gaussian Splatting. Flash-Split performs 2D reflection removal with a latent diffusion model.