7 papers
Extreme Blind Image Restoration via Prompt-Conditioned Information Bottleneck
Hongeun Kim, Bryan Sangwoo Kim, Jong Chul Ye
Blind Image Restoration (BIR) methods have achieved remarkable success but falter when faced with Extreme Blind Image Restoration (EBIR), where inputs suffer from severe, compounde…
Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Jeongsol Kim, Chanseok Lee, Jongin You +2
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a nove…
Diffusion models for inverse problems
Hyungjin Chung, Jeongsol Kim, Jong Chul Ye
Using diffusion priors to solve inverse problems in imaging have significantly matured over the years. In this chapter, we review the various different approaches that were propose…
FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing
Jeongsol Kim, Yeobin Hong, Jonghyun Park +1
Recent inversion-free, flow-based image editing methods such as FlowEdit leverages a pre-trained noise-to-image flow model such as Stable Diffusion 3, enabling text-driven manipula…
FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems
Jeongsol Kim, Bryan Sangwoo Kim, Jong Chul Ye
Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides…
Aligning Text to Image in Diffusion Models is Easier Than You Think
Jaa-Yeon Lee, Byunghee Cha, Jeongsol Kim +1
While recent advancements in generative modeling have significantly improved text-image alignment, some residual misalignment between text and image representations still remains.…