Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Office Monitoring and Surveillance System Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Dense Haze Removal Using Convolution Neural Network

verfasst von : Mayuri Dongare, Jyoti Kendule

Erschienen in: Techno-Societal 2020

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Pictures caught in murky climate show up low conversely. Debasement in the picture contrast is because of lessening in the light energy reflected from the scene object. In this paper, we propose a picture de-right of passage network which upgrades the perceivability of pictures caught in murky climate. The proposed network comprises of multi-scale convolution channels consolidated by commencement module to extricate the multi-scale highlights. Alongside the multi-scale highlight extraction, we propose a utilization of thick associations with engender learned highlights inside the origin modules. Combinely, the proposed network is planned by joining the standards of both initiation and thick module, along these lines, named as beginning thick organization. To prepare the proposed network for picture de-inception, we utilize primary similitude list metric alongside the L1 misfortune. Existing benchmark information bases are used to assess the favorable to presented network for picture de-right of passage. Exploratory examination shows that the proposed network beats the current methodologies for picture de-preliminaries.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Multi-Classification of Breast Histopathological Image Using Xception: Deep Learning with Depthwise Separable Convolutions Model
Nächstes Kapitel Diabetic Retinopathy Detection with Optimal Feature Selection: An Algorithmic Analysis
Literatur
1.
Dudhane A, Murala S (2019) Cardinal color fusion network for single image haze removal. Mach Vis Appl 30(2):231–242CrossRef
2.
Patil PW, Murala S (2018) Msfgnet: a novel compact end-to- end deep network for moving object detection. IEEE Trans actions Intell Transp Syst
3.
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 Proces 25(11):5187–5198MathSciNetCrossRef
4.
Dudhane A, Murala S (2018) IEEE, 2018, Cˆ 2msnet: A novel approach for single image haze removal. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 1397–1404
5.
Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M-H (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision. Springer, Berlin, pp 154–169
6.
Fattal R (2008) Single image dehazing. ACM Trans Graphics (TOG), 27(3):72
7.
Gibson KB, Vo DT, Nguyen TQ (2012) An investigation of dehazing effects on image and video coding. IEEE Trans Image Proces 21(2):662–673MathSciNetCrossRef
8.
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
9.
Huang S-C, Chen B-H, Wang W-J (2014) Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Trans Circuits Syst Video Technol 24(10):1814–1824CrossRef
10.
Ancuti CO, Ancuti C, Hermans C, Bekaert P (2010) A fast semi-inverse approach to detect and remove the haze from a single image. In: Asian conference on computer vision. Springer, Berlin, pp 501–514
11.
Tan RT (2008) Visibility in bad weather from a single image. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE pp 1–8
12.
Tang K, Yang J, Wang J (2014) Investigating haze-relevant features in a learning framework for image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2995–3000
13.
Zhu Q, Mai J, Shao L (2014) Single image dehazing using color attenuation prior. In: 25th British machine vision conference, BMVC
14.
Schechner YY, Narasimhan SG, Nayar SK (2001) Instant dehazing of images using polarization. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, CVPR 2001. vol 1, pp I–I
15.
Shwartz S, Namer E, Schechner YY (2006) Blind haze separation. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 2, pp 1984–1991
16.
Cozman F, Krotkov E (1997) Depth from scattering, Jun 1997. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 801–806
17.
Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: The proceedings of the Seventh IEEE international conference on computer vision, vol 2. IEEE, pp 820–827
18.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
19.
He K, Sun J, Tang X (2010) Guided image filtering. In: European conference on computer vision, Springer, Berlin, pp 1–14
20.
Yu J, Xiao C, Li D (2010) Physics-based fast single image fog removal. In: 2010 IEEE 10th international conference on signal processing (ICSP), IEEE, pp 1048–1052
21.
Lai Y, Chen Y, Chiou C, Hsu C (2015) Single-image dehazing via optimal transmission map under scene priors. IEEE Trans Circuits Syst Video Technol 25(1):1–14CrossRef
22.
Wang J, Lu K, Xue J, He N, Shao L (2018) Single image dehazing based on the physical model and msrcr algorithm. IEEE Trans Circ Syst Video Technol, pp 1–1
23.
Ancuti C, Ancuti CO, De Vleeschouwer C (2016) IEEE 2016, D-hazy: A dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE international conference on image processing (ICIP), pp 2226–2230
24.
He K, Sun J (2015) Convolutional neural networks at con- strained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5353–5360
25.
Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: All- in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision. pp 4770–4778
26.
Swami K, Das SK (2018) Candy: conditional adversarial networks based end-to-end system for single image haze removal. In: 2018 24th international conference on pattern recognition (ICPR). IEEE, pp 3061–3067
27.
Dudhane A, Murala S (2019) IEEE, 2019, Cdnet: Single image de-hazing using unpaired adversarial training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 1147–1155
28.
Engin D, Genc A, Kemal Ekenel H (2018) Cycle-dehaze: Enhanced cyclegan for single image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 825–833
29.
Patil P, Murala S (2019) Fggan: A cascaded unpaired learning for background estimation and foreground segmentation. In: 2019 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1770–1778
30.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
31.
Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:​1607.​08022
32.
Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5967–5976
33.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to struc tural similarity. IEEE Trans Image Proces 13(4):600–612CrossRef
34.
Sharma G, Wu W, Dalal EN (2005) The ciede2000 color- difference formula: Implementation notes, supplementary test data, and mathematical observations. In: Color research & application: endorsed by inter-society color council, the colour group (Great Britain), Canadian society for color, color science association of Japan, Dutch society for the study of color, The Swedish colour centre foundation, colour society of Australia, Centre Franc¸ais de la Couleur 30(1):21–30
35.
Yang X, Xu Z, Luo J (2018) Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In: In Thirty third-second AAAI conference on Artificial Intelligence (AAAI-18)
36.
Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image- to-image translation using cycle-consistent adversarial net- works. In: 2017 IEEE international conference on computer vision (ICCV). IEEE, pp 2242–2251
37.
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2019) Benchmarking single-image dehazing and be- yond. IEEE Trans Image Proces 28(1):492–505CrossRef
38.
Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: European conference on computer vision. Springer, pp 746–760
39.
40.
Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M-H (2018) Gated fusion network for single image dehazing. In: The IEEE conference on computer vision and pattern recognition (CVPR)
41.
Zhang H, Patel VM (2018) Densely connected pyramid de-hazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3194–3203
42.
Yang D, Sun J (2018) Proximal dehaze-net: a prior learning- based deep network for single image dehazing. In: Proceedings of the European conference on computer vision (ECCV), pp 702–717
43.
Hu H-M, Guo Q, Zheng J, Wang H, Li B (2019) Single image defogging based on illumination decomposition for visual maritime surveillance. IEEE Trans Image Process
44.
Ren W, Pan J, Zhang H, Cao X, Yang M-H (2019) Single image dehazing via multi-scale convolutional neural net- works with holistic edges. Int J Comput Vision, pp 1–20
45.
Dudhane A, Murala S (2019) Ryf-net: Deep fusion network for single image haze removal. IEEE Trans Image Proces 29:628–640
46.
Dudhane A, Singh Aulakh H, Murala S (2019) Ri-gan: An end-to-end network for single image haze removal. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 0–0
47.
Chen S, Chen Y, Qu Y, Huang J, Hong M (2019) Multi-scale adaptive dehazing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition work- shops, pp 0–0
48.
Guo T, Li X, Cherukuri V, Monga V (2019) Dense scene information estimation network for dehazing. In: Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, pp 0–0
Metadaten
Titel
Dense Haze Removal Using Convolution Neural Network
verfasst von
Mayuri Dongare
Jyoti Kendule
Copyright-Jahr
2021
Buch
Techno-Societal 2020

Print ISBN: 978-3-030-69920-8

Electronic ISBN: 978-3-030-69921-5

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-69921-5_55

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise