  Image enhancement holds extensive applications in real-world scenarios due to
complex environments and limitations of imaging devices. Conventional methods
are often constrained by their tailored models, resulting in diminished
robustness when confronted with challenging degradation conditions. In
response, we propose FlowIE, a simple yet highly effective flow-based image
enhancement framework that estimates straight-line paths from an elementary
distribution to high-quality images. Unlike previous diffusion-based methods
that suffer from long-time inference, FlowIE constructs a linear many-to-one
transport mapping via conditioned rectified flow. The rectification straightens
the trajectories of probability transfer, accelerating inference by an order of
magnitude. This design enables our FlowIE to fully exploit rich knowledge in
the pre-trained diffusion model, rendering it well-suited for various
real-world applications. Moreover, we devise a faster inference algorithm,
inspired by Lagrange's Mean Value Theorem, harnessing midpoint tangent
direction to optimize path estimation, ultimately yielding visually superior
results. Thanks to these designs, our FlowIE adeptly manages a diverse range of
enhancement tasks within a concise sequence of fewer than 5 steps. Our
contributions are rigorously validated through comprehensive experiments on
synthetic and real-world datasets, unveiling the compelling efficacy and
efficiency of our proposed FlowIE. Code is available at
https://github.com/EternalEvan/FlowIE.
