Combating Bad Weather Part II : Fog Removal from Image and Video
Contributor(s): Tripathi, Abhishek Kumar.Material type: TextSeries: eBooks on Demand.Synthesis Lectures on Image, Video, and Multimedia Processing: Publisher: San Rafael : Morgan & Claypool Publishers, 2015Description: 1 online resource (86 p.).ISBN: 9781627055871.Subject(s): Computer vision | Digital video | Fog -- Pictorial works | Image processing -- Digital techniquesGenre/Form: Electronic books.Additional physical formats: Print version:: Combating Bad Weather Part II : Fog Removal from Image and VideoDDC classification: 621.367 Online resources: Click here to view this ebook.
|Item type||Current location||Call number||URL||Status||Date due||Barcode|
|Electronic Book||UT Tyler Online Online||TA1637 .M384 2015 (Browse shelf)||http://uttyler.eblib.com/patron/FullRecord.aspx?p=1925650||Available||EBL1925650|
Acknowledgments; Introduction; Video Post-Processing; Motivation; Analysis of Fog; Overview; Framework; Dataset and Performance Metrics ; Foggy Images and Videos; Performance Metrics; Contrast Gain (C_gain); Percentage of the Number of Saturated Pixels (); Computation Time; Root Mean Square (RMS) Error; Perceptual Quality Metric (PQM); Important Fog Removal Algorithms; Enhancement-based Methods; Restoration-based Methods; Multiple Image-based Restoration Techniques; Single Image-based Restoration Techniques; Single-Image Fog Removal Using an Anisotropic Diffusion; Introduction
Fog Removal AlgorithmInitialization of Airlight Map; Airlight Map Refinement; Behavior of Anisotropic Diffusion; Restoration; Post-processing; Simulation and Results; Conclusion; Video Fog Removal Framework Using an Uncalibrated Single Camera System; Introduction; Challenges of Realtime Implementation; Video Fog Removal Framework; MPEG Coding; Simulation and Results; Conclusion; Conclusions and Future Directions; Bibliography; Authors' Biographies
Every 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
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