Unifying Deep Stochastic Processes for Image Enhancement
Deep stochastic processes have recently become a central paradigm for image enhancement, with many methods explicitly conditioning the stochastic trajectory on the degraded input. However, the relationship between these conditional processes and standard diffusion models remains unclear. In this work, we introduce a unified perspective on stochastic image enhancement by classifying recent methods into three families of continuous-time processes: unconditional diffusion models, Ornstein-Uhlenbeck (OU) processes, and diffusion bridges. We show that all of these approaches arise from a common stochastic differential equation (SDE) formulation. This framework makes explicit that seemingly dis...
| Authors | Wojciech Kozłowski, Radosław Kuczbański, Kamil Adamczewski et al. |
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| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 18 August 2026 |
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