Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2023

24-12-2022 | Original Article

Progressive image dehazing network based on dual feature extraction modules

Authors: Yong Yang, Wei Hu, Shuying Huang, Weiguo Wan, Juwei Guan

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Image dehazing is of great importance and has been widely studied, as haze severely affects many high-level computer vision tasks. In this paper, by considering the gradual dissipation process of haze, a progressive dehazing network (PDN) is proposed. The proposed approach realizes haze removal step by step by constructing two main modules: the preliminary and fine dehazing modules. In the preliminary dehazing module, a combined residual block is first constructed to extract and enhance features of different levels. Then, an adaptive feature fusion strategy is designed to integrate these features and output the initial dehazing result. Aiming at the residual haze in the initial results, a fine dehazing module is constructed by simulating the last period of the haze dissipation process to further extract a fine haze layer. The final dehazing result is obtained by removing the fine haze layer from the initial dehazing result. Experimental results indicate that the proposed method is superior to some state-of-the-art dehazing methods in terms of visual comparison and objective evaluation.
previous article Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction
next article On development of multimodal named entity recognition using part-of-speech and mixture of experts

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Appendix
Available only for authorised users
Literature
1.
Dudhane A, Biradar K, Patil P, Hambarde P, Murala S (2020) Varicolored image de-hazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 13–19
2.
Yao L, Xu H, Zhang W, Liang X, Li Z (2020) SM-NAS: structural-to-modular neural architecture search for object detection. In: AAAI Conf Artif Intell, pp 12661–12668
3.
Xu Y, Fu M, Wang Q, Wang Y, Chen K, Xia G, Bai X (2021) Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Trans Pattern Anal Mach Intell 43(4):1452–1459CrossRef
4.
Deng W, Zheng L, Ye Q, Kang G, Yi Y, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proc. IEEE Conf Comput Vis Pattern Recognit, pp 994–100
5.
Al-Sammaraie M (2015) Contrast enhancement of roads images with foggy scenes based on histogram equalization. In: Proc. IEEE ICCSE, pp 95–101
6.
Yang W, Wang R, Fang S, Zhang X (2010) Variable filter Retinex algorithm for foggy image enhancement. J Comput Aided Des Comput Graph 22(6):965–971
7.
Jia J, Yue H (2014) A wavelet-based approach to improve foggy image clarity. In: Proc. IFAC, pp 930–935
8.
Cantor A (1978) Optics of the atmosphere: scattering by molecules and particles. IEEE J Quantum Electron 14(9):698–699CrossRef
9.
Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 1674–1682
10.
11.
He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353CrossRef
12.
Tufail Z, Khurshid K, Salman A, Nizami I, Khurshid K, Jeon B (2018) Improved dark channel prior for image defogging using RGB and YCbCr color space. IEEE Access 6(1):32576–32587CrossRef
13.
Chen W, Ding J, Kuo S (2019) PMS-Net: robust haze removal based on patch map for single images. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 11673–116821
14.
Chen D, He M, Fan Q, Liao J, Zhang L, Hou D, Yuan L, Hua G (2019) Gated context aggregation network for image dehazing and deraining. In: Proc IEEE Winter Conf Appl Comput Vis (WACV), pp 1375–1383
15.
Qu Y, Chen Y, Huang J, Xie Y (2019) Enhanced Pix2pix dehazing network. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 8160–8168
16.
Pang Y, Nie J, Xie J, Han J, Li X (2020) BidNet: binocular image dehazing without explicit disparity estimation. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 5930–5939
17.
Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 2805–2814
18.
Li R, Pan J, He M, Li Z, Tang J (2020) Task-Oriented Network for Image Dehazing. IEEE Trans Image Process 29:6523–6534CrossRefMATH
19.
Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M (2016) Single image dehazing via multi-scale convolutional neural networks. In: Proc European Conf Comput Vis (ECCV), pp 154–169
20.
Cai B, Xu X, Jia K, Qing C, Tao D (2016) DehazeNet: An end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198MathSciNetCrossRefMATH
21.
Li B, Peng X, Wang Z, Xu J, Feng D (2017) AOD-Net: all-in-one dehazing network. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 4780–4788
22.
Zhang H, Patel V (2018) Densely connected pyramid dehazing network. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 3194–3203
23.
Guo T, Monga V (2020) Reinforced depth-aware deep learning for single image dehazing. In: Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP), pp 8891–8895
24.
Lee B, Lee K, Oh J, Kweon I (2020) CNN-based simultaneous dehazing and depth estimation. In: Proc IEEE Int Conf Robot Autom (ICRA), pp 9722–9728
25.
Wang C, Huang Y, Zou Y, Xu Y (2021) FWB-NET:front white balance network for color shift correction in single image dehazing via atmospheric light estimation. In: Proc. IEEE Int Conf Acoust Speech Signal Process (ICASSP), pp 2040–2044
26.
Ullah H, Muhammad K, Irfan M, Anwar S, Sajjad M, Imran A, Albuquerque V (2021) Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing. IEEE Trans Image Process 30:8968–8982CrossRef
27.
VaswaniA, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: NeurIPS, pp 1–11
28.
29.
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 10012–10022
30.
Li K, Yu R, Wang Z, Yuan L, Song G, Chen J (2022) Locality guidance for improving vision transformers on tiny datasets. arXiv preprint arXiv:​2207.​10026
31.
Yang Y, Jiao L, Liu X, Fang L, Yang S, Feng Z, Tang X (2022) Transformers meet visual learning understanding: a comprehensive review. arXiv preprint arXiv:​2203.​12944
32.
Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(11):600–612CrossRef
33.
Wang Z, Simoncelli E, Bovik A (2003) Multiscale structural similarity for image quality assessment. In: Proc. 37th Asilomar Conf Signals Syst Comput, pp 1398–1402
34.
Sharma G, Wu W, Dalal E (2005) The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30CrossRef
35.
Liu X, Ma Y, Shi Z, Chen J (2019) GridDehazeNet: attention-based multi-scale network for image dehazing. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 7313–7322
36.
Hong M, Xie Y, Li C, Qu Y (2020) Distilling image dehazing with heterogeneous task imitation. In: Proc. IEEE Conf Comput Vis Pattern Recognit, pp 3459–3468
37.
Liu Z, Xiao B, Alrabeiah M, Wang K, Chen J (2019) Single image dehazing with a generic model agnostic convolutional neural network. IEEE Signal Process Lett 26(6):833–837CrossRef
38.
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single image dehazing and beyond. IEEE Trans Image Process 28(1):492–505MathSciNetCrossRefMATH
39.
Zhang Y, Ding L, Sharma G (2017) HazeRD: an outdoor scene dataset and benchmark for single image dehazing. In: Proc IEEE Int Conf Image Process (ICIP), pp 3205–3209
40.
Zhu X, Lyu S, Wang X et al (2021) TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 2778–2788
41.
Lin T, Maire M, Belongie S et al (2014) Microsoft COCO: common objects in context. In: Proc European Conf Comput Vis (ECCV), pp 740–755
Metadata
Title
Progressive image dehazing network based on dual feature extraction modules
Authors
Yong Yang
Wei Hu
Shuying Huang
Weiguo Wan
Juwei Guan
Publication date
24-12-2022
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2023
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-022-01753-x

Other articles of this Issue 6/2023

International Journal of Machine Learning and Cybernetics 6/2023 Go to the issue

Original Article

Multiview nonnegative matrix factorization with dual HSIC constraints for clustering

Original Article

Enhanced feature Fusion structure of YOLO v5 for detecting small defects on metal surfaces

Original Article

GCN recommendation model based on the fusion of dynamic multiple-view latent interest topics

Original Article

Robust non-negative supervised low-rank discriminant embedding (NSLRDE) for feature extraction

Original Article

Multilayer discriminative extreme learning machine for classification

Original Article

Method for three-way decisions using similarity in incomplete information systems