Disentangling Generation and Regression in Stochastic Interpolants for Controllable Image Restoration
arXiv:2605.21381v1 Announce Type: cross Abstract: Recent advances in Image Restoration (IR) have been largely driven by generative methods such as Diffusion Models and Flow Matching, which excel in synthesizing realistic textures while suffering from slow multi-step inference and compromised pixel fidelity. In contrast, classical regression-based IR methods excel precisely in these aspects, offering single-step efficiency and high pixel-level reconstruction fidelity. To bridge this gap, we propose DiSI, a unified framework that Disentangles the underlying Stochastic Interpolant process into in
The continuous push for more efficient and accurate AI models drives innovation in foundational areas like image restoration, bridging the gap between generative and regression-based methods.
Improving image restoration technologies has broad implications for various AI applications, from computer vision to graphics, impacting industries reliant on visual data analysis and generation.
This advancement proposes a new framework that potentially combines the best aspects of generative models (realism) and regression methods (efficiency, fidelity) for image restoration, leading to more versatile tools.
- · AI/ML researchers
- · Computer Vision developers
- · Creative industries (e.g., film, gaming)
- · Medical imaging sector
- · Inefficient single-paradigm image restoration solutions
- · Hardware constrained by slow generative models
Wider adoption of more robust and efficient image restoration techniques across various applications.
Accelerated development of AI systems that can operate with higher visual fidelity and speed in real-world scenarios.
New commercial products and services emerge leveraging advanced image processing capabilities that were previously impractical.
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