000 04506nam a22005175i 4500
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020 _a9783031022524
_9978-3-031-02252-4
024 7 _a10.1007/978-3-031-02252-4
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aMukhopadhyay, Sudipta.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987131
245 1 0 _aCombating Bad Weather Part II
_h[electronic resource] :
_bFog Removal from Image and Video /
_cby Sudipta Mukhopadhyay, Abhishek Kumar Tripathi.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXIII, 70 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
505 0 _aAcknowledgments -- Introduction -- Analysis of Fog -- Dataset and Performance Metrics -- Important Fog Removal Algorithms -- Single-Image Fog Removal Using an Anisotropic Diffusion -- Video Fog Removal Framework Using an Uncalibrated Single Camera System -- Conclusions and Future Directions -- Bibliography -- Authors' Biographies .
520 _aEvery year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execution time. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_987132
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_987134
650 2 4 _aElectrical and Electronic Engineering.
_987136
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aTripathi, Abhishek Kumar.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987137
710 2 _aSpringerLink (Online service)
_987138
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011245
776 0 8 _iPrinted edition:
_z9783031033803
830 0 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
_987139
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02252-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c86055
_d86055