AOD-Net: All-in-One Dehazing Network

Li, Boyi; Peng, Xiulian; Wang, Zhangyang; Xu, Jizheng; Feng, Dan

doi:10.1109/iccv.2017.511

Cited by 1,622 publications

(1,329 citation statements)

References 24 publications

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“…We adopt PSNR and SSIM for the quantitative evaluation and compare the proposed model with several state-of-the-art dehazing methods: Color Attenuation Prior (CAP) [2], AOD-Net [7], Multi-band enhancement [5], and Wavelet U-net (W U-net) [11]. CAP and MBE are prior-based methods, and the other two methods belong to deep-learning-based methods.…”

Section: Image Dehazing Resultsmentioning

confidence: 99%

Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

Yang

Tsai

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally, we propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step. In the proposed loss function, discrete wavelet transforms are applied repeatedly to divide the image into differently sized patches with different frequencies and scales. The proposed loss function is the accumulation of SSIM loss of various patches with respective ratios. Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms stateof-the-art algorithms. Code and models are available at https://github.com/dectrfov/Y-net Index Terms-Single image dehazing, Y-net, discrete wavelet transform, structure similarity, multi-scale feature aggregation

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Section: Image Dehazing Resultsmentioning

confidence: 99%

Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

Yang

Tsai

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The proposed network is tested on the synthetic dataset for qualitative and quantitative comparisons with the state-of-the-arts that include DCP [9], DehazeNet [1], MSCNN [26], AOD-Net [13] and GFN [27]. The DCP is a prior-based method and is regarded as the baseline in single image dehazing.…”

Section: Synthetic Datasetmentioning

confidence: 99%

GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing

Liu

Shi

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

703

393

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We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to those derived inputs produced by handselected pre-processing methods. The backbone module implements a novel attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-arts on both synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned. Project website: https: //proteus1991.github.io/GridDehazeNet/.

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“…The Rain drop (T) set was borrowed from [7]'s released training set consisting of 861 pairs of clean and rain-drop corrupted images, upon their authors' consent. The Rain and mist (T) set is synthesized by first adding haze using the atmospheric scattering model: for each clean image, we estimate depth using the algorithm in [35,36] as recommended by [37], set different atmospheric lights A by choosing each channel uniformly randomly between [0.7, 1.0], and select β uniformly at random between [0.6, 1.8]. Then from the synthesized hazy version, we further add rain streaks in the same way as Rain streak (T).…”

Section: Training Sets: Three Synthesis Modelsmentioning

confidence: 99%

Single Image Deraining: A Comprehensive Benchmark Analysis

Liu

Cao

Araujo

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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279

161

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Numerous single image deraining algorithms have been recently proposed. However, these algorithms are mainly evaluated using certain type of synthetic images, assuming a specific rain model, plus a few real images. It is thus unclear how these algorithms would perform on rainy images acquired "in the wild" and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new largescale benchmark consisting of both synthetic and realworld rainy images of various rain types. This dataset highlights diverse rain models (rain streak, rain drop, rain and mist), as well as a rich variety of evaluation criteria (fulland no-reference objective, subjective, and task-specific) Our evaluation and analysis indicate the performance gap between synthetic rainy images and real-world images and allow us to better identify the strengths and limitations of each method as well as future research directions.

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AOD-Net: All-in-One Dehazing Network

Cited by 1,622 publications

References 24 publications

Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing

Single Image Deraining: A Comprehensive Benchmark Analysis

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