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Combating Bad Weather Part II - Fog Removal from Image and Video (Paperback)
Loot Price: R812
Discovery Miles 8 120
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Combating Bad Weather Part II - Fog Removal from Image and Video (Paperback)
Series: Synthesis Lectures on Image, Video, and Multimedia Processing
Expected to ship within 10 - 15 working days
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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 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.
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