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eess.IV2025
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…
eess.IV2025
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…
eess.IV2025
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…