4 papers
WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting
Lezhong Wang, Mehmet Onurcan Kaya, Siavash Bigdeli +1
Recent single-image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, their effectiveness in the co…
I2I-PR: Deep Iterative Refinement for Phase Retrieval using Image-to-Image Diffusion Models
Mehmet Onurcan Kaya, Figen S. Oktem
Phase retrieval aims to recover a signal from intensity-only measurements, a fundamental problem in many fields such as imaging, holography, optical computing, crystallography, and…
prNet: Data-Driven Phase Retrieval via Stochastic Refinement
Mehmet Onurcan Kaya, Figen S. Oktem
Phase retrieval is an ill-posed inverse problem in which classical and deep learning-based methods struggle to jointly achieve measurement fidelity and perceptual realism. We propo…
DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models
Mehmet Onurcan Kaya, Figen S. Oktem
Diffusion models have demonstrated their utility as learned priors for solving various inverse problems. However, most existing approaches are limited to linear inverse problems. T…