000 06209nam a2200781 i 4500
001 7039333
003 IEEE
005 20200413152916.0
006 m eo d
007 cr cn |||m|||a
008 150117s2015 caua foab 000 0 eng d
020 _a9781627055871
_qebook
020 _z9781627055864
_qprint
024 7 _a10.2200/S00601ED1V01Y201410IVM017
_2doi
035 _a(CaBNVSL)swl00404600
035 _a(OCoLC)900340905
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTA1637
_b.M8532 2015
082 0 4 _a621.367
_223
100 1 _aMukhopadhyay, Sudipta.,
_eauthor.
245 1 0 _aCombating bad weather.
_nPart II,
_pFog removal from image and video /
_cSudipta Mukhopadhyay, Abhishek Kumar Tripathi.
246 3 0 _aFog removal from image and video.
264 1 _aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_c2015.
300 _a1 PDF (xiii, 70 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on image, video, and multimedia processing,
_x1559-8144 ;
_v# 17
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 66-69).
505 0 _a1. Introduction -- 1.1 Video post-processing -- 1.2 Motivation --
505 8 _a2. Analysis of fog -- 2.1 Overview -- 2.1.1 Framework --
505 8 _a3. Dataset and performance metrics -- 3.1 Foggy images and videos -- 3.2 Performance metrics -- 3.2.1 Contrast gain (Cgain) -- 3.2.2 Percentage of the number of saturated pixels -- 3.2.3 Computation time -- 3.2.4 Root mean square (RMS) error -- 3.2.5 Perceptual quality metric (PQM) --
505 8 _a4. Important fog removal algorithms -- 4.1 Enhancement-based methods -- 4.2 Restoration-based methods -- 4.2.1 Multiple image-based restoration techniques -- 4.2.2 Single image-based restoration techniques --
505 8 _a5. Single-image fog removal using an anisotropic diffusion -- 5.1 Introduction -- 5.2 Fog removal algorithm -- 5.2.1 Initialization of airlight map -- 5.2.2 Airlight map refinement -- 5.2.3 Behavior of anisotropic diffusion -- 5.2.4 Restoration -- 5.2.5 Post-processing -- 5.3 Simulation and results -- 5.4 Conclusion --
505 8 _a6. Video fog removal framework using an uncalibrated single camera system -- 6.1 Introduction -- 6.2 Challenges of realtime implementation -- 6.3 Video fog removal framework -- 6.3.1 MPEG coding -- 6.4 Simulation and results -- 6.5 Conclusion --
505 8 _a7. Conclusions and future directions -- Bibliography -- Authors' biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _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.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on January 17, 2015).
650 0 _aImage processing
_xDigital techniques.
650 0 _aDigital video.
650 0 _aComputer vision.
650 0 _aFog
_xPictorial works.
653 _abad weather
653 _aimage enhancement
653 _afog
653 _aattenuation
653 _aairlight
653 _aatmospheric visibility
653 _aanisotropic diffusion
653 _aimage contrast
653 _atemporal redundancy
653 _avideo enhancement
653 _aoutdoor vision and weather
700 1 _aTripathi, Abhishek Kumar.,
_eauthor.
776 0 8 _iPrint version:
_z9781627055864
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on image, video, and multimedia processing ;
_v# 17.
_x1559-8144
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7039333
999 _c562110
_d562110