Unifying Deep Stochastic Processes for Image Enhancement

2026-05-02

AI Summary

This work addresses the lack of a unified theoretical framework in existing image enhancement methods, which has led to ambiguous relationships and hindered fair comparisons. The authors propose the first unified perspective grounded in continuous-time stochastic differential equations (SDEs), systematically categorizing prevailing approaches into three classes: unconditional diffusion models, Ornstein–Uhlenbeck processes, and diffusion bridges. This formulation clarifies their fundamental differences in drift terms, diffusion coefficients, terminal distributions, and boundary conditions. Through controlled experiments using a consistent architecture and training protocol, the study reveals that performance is primarily governed by specific design choices rather than the methodological category itself. To facilitate standardized research, the authors also release ItoVision, an open-source modular library enabling fair evaluation and rapid prototyping.

Abstract

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 disparate methods differ primarily in their drift and diffusion terms, terminal distributions, and boundary conditions, while schedulers and samplers constitute orthogonal design choices. Leveraging this unification, we conduct a controlled empirical study across multiple image enhancement tasks using identical architectures and training protocols. Our results reveal no consistently dominant method; instead, we identify and disentangle the specific design choices that most strongly influence performance. Finally, we release ItoVision, a modular PyTorch library that implements the unified framework and enables rapid prototyping and fair comparison of stochastic image enhancement methods.

Problem

• deep stochastic processes
• image enhancement
• diffusion models
• stochastic differential equations
• conditional generation

Innovation

• stochastic differential equation
• diffusion bridge
• Ornstein-Uhlenbeck process
• unified framework
• image enhancement