Benchmarking Single Image Dehazing and Beyond

IEEE Transactions on Image Processing (TIP), 2019

New Version(Benchmarking Single Image Dehazing and Beyond)

Abstract: In this paper, we present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purpose]s. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.

Paper

[PDF]

Dataset

Standard Version: [RESIDE-Standard]

Extended Version : [RESIDE-β]

Bibtex

@article{li2019benchmarking,

title={Benchmarking Single-Image Dehazing and Beyond},

author={Li, Boyi and Ren, Wenqi and Fu, Dengpan and Tao, Dacheng and Feng, Dan and Zeng, Wenjun and Wang, Zhangyang},

journal={IEEE Transactions on Image Processing},

volume={28},

number={1},

pages={492--505},

year={2019},

publisher={IEEE}

}

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Old Version (RESIDE: A Benchmark for Single Image Dehazing)

Abstract: In this paper, we present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE sheds light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.