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007			cr nn 008mamaa
008			230330s2015 sz | s |||| 0|eng d
020			_a9783031022524
024	7		_a10.1007/978-3-031-02252-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637 _b2015 EB
100	1		_aMukhopadhyay, Sudipta _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687908
245	1	0	_aCombating Bad Weather Part II : _bFog Removal from Image and Video _cby Sudipta Mukhopadhyay, Abhishek Kumar Tripathi
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XIII, 70 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9159793 _aVisión por ordenador
700	1		_aTripathi, Abhishek Kumar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687909
776	0	8	_iPrinted edition: _z9783031011245
776	0	8	_iPrinted edition: _z9783031033803
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02252-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI