# FlowIE: Efficient Image Enhancement via Rectified Flow

arXiv: https://arxiv.org/abs/2406.00508 · HTML: https://mlsanity.com/paper/2406.00508/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 3 of its 58 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 3 of 3)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Rectified Flow: A Marginal Preserving Approach to Optimal Transport](https://mlsanity.com/paper/2209.14577/) (2022)
- [DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior](https://mlsanity.com/paper/2308.15070/) (2023)

## Built on by (most cited first, 30 of 38)

- [Diffusion Models in Low-Level Vision: A Survey](https://mlsanity.com/paper/2406.11138/) (2024)
- [KV-Edit: Training-Free Image Editing for Precise Background Preservation](https://mlsanity.com/paper/2502.17363/) (2025)
- [Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration](https://mlsanity.com/paper/2410.00418/) (2024)
- [Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality](https://mlsanity.com/paper/2503.00266/) (2025)
- [Efficient Rectified Flow for Image Fusion](https://mlsanity.com/paper/2509.16549/) (2025)
- [Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs](https://mlsanity.com/paper/2409.17778/) (2024)
- [SparseDiT: Token Sparsification for Efficient Diffusion Transformer](https://mlsanity.com/paper/2412.06028/) (2024)
- [ScoreHOI: Physically Plausible Reconstruction of Human-Object Interaction via Score-Guided Diffusion](https://mlsanity.com/paper/2509.07920/) (2025)
- [Efficient Image Restoration via Latent Consistency Flow Matching](https://mlsanity.com/paper/2502.03500/) (2025)
- [Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration](https://mlsanity.com/paper/2504.15159/) (2025)
- [Coherent Audio-Visual Editing via Conditional Audio Generation Following Video Edits](https://mlsanity.com/paper/2512.07209/) (2025)
- [Flow Straight and Fast in Hilbert Space: Functional Rectified Flow](https://mlsanity.com/paper/2509.10384/) (2025)
- [HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing](https://mlsanity.com/paper/2509.18190/) (2025)
- [LORE: Latent Optimization for Precise Semantic Control in Rectified Flow-based Image Editing](https://mlsanity.com/paper/2508.03144/) (2025)
- [Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling](https://mlsanity.com/paper/2506.08796/) (2025)
- [Deep Neural Networks Inspired by Differential Equations](https://mlsanity.com/paper/2510.09685/) (2025)
- [What Your Features Reveal: Data-Efficient Black-Box Feature Inversion Attack for Split DNNs](https://mlsanity.com/paper/2511.15316/) (2025)
- [Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration](https://mlsanity.com/paper/2412.12550/) (2024)
- [scSplit: Bringing Severity Cognizance to Image Decomposition in Fluorescence Microscopy](https://mlsanity.com/paper/2503.22983/) (2025)
- [Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution](https://mlsanity.com/paper/2511.01175/) (2025)
- [VARestorer: One-Step VAR Distillation for Real-World Image Super-Resolution](https://mlsanity.com/paper/2604.21450/) (2026)
- [PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography](https://mlsanity.com/paper/2606.23226/) (2026)
- [Flow Straight to Reality: Perceptually Consistent Flow Matching for Efficient Image Restoration](https://mlsanity.com/paper/2608.10544/) (2026)
- [FreeDNA: Endowing Domain Adaptation of Diffusion-Based Dense Prediction with Training-Free Domain Noise Alignment](https://mlsanity.com/paper/2506.22509/) (2025)
- [Latent Posterior-Mean Rectified Flow for Higher-Fidelity Perceptual Face Restoration](https://mlsanity.com/paper/2507.00447/) (2025)
- [Unsupervised Real-World Super-Resolution via Rectified Flow Degradation Modelling](https://mlsanity.com/paper/2508.07214/) (2025)
- [InfraDiffusion: zero-shot depth map restoration with diffusion models and prompted segmentation from sparse infrastructure point clouds](https://mlsanity.com/paper/2509.03324/) (2025)
- [VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring](https://mlsanity.com/paper/2509.08392/) (2025)
- [ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows](https://mlsanity.com/paper/2509.22695/) (2025)
- [Enhancing Blind Face Restoration through Online Reinforcement Learning](https://mlsanity.com/paper/2509.23339/) (2025)

## Abstract (arXiv)

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.