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Related Experiment Video

Updated: Jan 13, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Hazediff: A training-free diffusion-based image dehazing method with pixel-level feature injection.

Xiaoxia Lin¹, Zhengao Li¹, Dawei Huang¹

¹College of Intelligent Equipment, Shandong University of Science and Technology, Taian, China.

|October 28, 2025

Summary

This summary is machine-generated.

HazeDiff, a novel training-free image dehazing method, overcomes limitations of current approaches by eliminating the need for paired datasets. This diffusion model-based technique enhances image quality and generalization for clearer visual tasks.

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Last Updated: Jan 13, 2026

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Published on: February 8, 2014

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Recurrent haze phenomena degrade image quality, hindering mid- and high-level visual tasks.
Current image dehazing methods face challenges with data acquisition and generalization.

Purpose of the Study:

To propose HazeDiff, a training-free image dehazing method based on the Diffusion model.
To overcome limitations of existing data-dependent dehazing techniques.

Main Methods:

HazeDiff utilizes a Diffusion model, eliminating the need for paired training data.
Pixel-Level Feature Inject (PFI) integrates reference image features into the diffusion process.
Structure Retention Model (SRM) enhances features and retains structural integrity.

Main Results:

HazeDiff surpasses state-of-the-art methods on real-world and synthetic datasets.
Achieved higher scores on no-reference (NIQE) and full-reference (PSNR) metrics.
Demonstrated superior generalization ability and practicality in restoring high-quality images.

Conclusions:

HazeDiff offers a reliable and effective solution for image dehazing.
The training-free approach reduces computational costs and improves model stability.
Restored images exhibit natural visual features and clear structural content.