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Related Experiment Video

Updated: Oct 14, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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Published on: June 18, 2021

Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning.

Haoran Wei, Qingbo Wu, Hui Li

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 4, 2021

Summary

This summary is machine-generated.

This study introduces a Non-Homogeneous Haze Removal Network (NHRN) to effectively remove haze from single images. The novel approach uses artificial scene priors and bidimensional graph reasoning for superior image dehazing and understanding.

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Last Updated: Oct 14, 2025

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Published on: February 8, 2014

Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Single image dehazing is challenging due to lack of scene prior and potential distortion.
Real-world haze often exhibits non-homogeneous distribution, offering clues in preserved regions.

Purpose of the Study:

To propose a novel Non-Homogeneous Haze Removal Network (NHRN) for effective single image dehazing.
To leverage artificial scene priors and bidimensional graph reasoning for improved haze removal.

Main Methods:

Iterative gamma correction to simulate diverse haze conditions and enrich scene priors.
Bidimensional graph reasoning for non-local filtering across spatial and channel dimensions.
Modeling long-range dependencies and propagating scene priors between clear and hazy regions.

Main Results:

The NHRN method demonstrates superior performance compared to state-of-the-art algorithms.
Achieved excellent results on benchmark datasets for single image dehazing.
Showcased effectiveness in hazy image understanding tasks.

Conclusions:

The proposed NHRN framework successfully addresses non-homogeneous haze removal.
Bidimensional graph reasoning is a novel and effective approach for image dehazing.
The method offers significant advancements in both image dehazing and hazy image understanding.