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Combating Bad Weather Part I - Rain Removal from Video (Paperback)
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Combating Bad Weather Part I - Rain Removal from Video (Paperback)
Series: Synthesis Lectures on Image, Video, and Multimedia Processing
Expected to ship within 10 - 15 working days
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Current vision systems are designed to perform in normal weather
condition. However, no one can escape from severe weather
conditions. Bad weather reduces scene contrast and visibility,
which results in degradation in the performance of various computer
vision algorithms such as object tracking, segmentation and
recognition. Thus, current vision systems must include some
mechanisms that enable them to perform up to the mark in bad
weather conditions such as rain and fog. Rain causes the spatial
and temporal intensity variations in images or video frames. These
intensity changes are due to the random distribution and high
velocities of the raindrops. Fog causes low contrast and whiteness
in the image and leads to a shift in the color. This book has
studied rain and fog from the perspective of vision. The book has
two main goals: 1) removal of rain from videos captured by a moving
and static camera, 2) removal of the fog from images and videos
captured by a moving single uncalibrated camera system. The book
begins with a literature survey. Pros and cons of the selected
prior art algorithms are described, and a general framework for the
development of an efficient rain removal algorithm is explored.
Temporal and spatiotemporal properties of rain pixels are analyzed
and using these properties, two rain removal algorithms for the
videos captured by a static camera are developed. For the removal
of rain, temporal and spatiotemporal algorithms require fewer
numbers of consecutive frames which reduces buffer size and delay.
These algorithms do not assume the shape, size and velocity of
raindrops which make it robust to different rain conditions (i.e.,
heavy rain, light rain and moderate rain). In a practical
situation, there is no ground truth available for rain video. Thus,
no reference quality metric is very useful in measuring the
efficacy of the rain removal algorithms. Temporal variance and
spatiotemporal variance are presented in this book as no reference
quality metrics. An efficient rain removal algorithm using
meteorological properties of rain is developed. The relation among
the orientation of the raindrops, wind velocity and terminal
velocity is established. This relation is used in the estimation of
shape-based features of the raindrop. Meteorological property-based
features helped to discriminate the rain and non-rain pixels. Most
of the prior art algorithms are designed for the videos captured by
a static camera. The use of global motion compensation with all
rain removal algorithms designed for videos captured by static
camera results in better accuracy for videos captured by moving
camera. Qualitative and quantitative results confirm that
probabilistic temporal, spatiotemporal and meteorological
algorithms outperformed other prior art algorithms in terms of the
perceptual quality, buffer size, execution delay and system cost.
The work presented in this book can find wide application in
entertainment industries, transportation, tracking and consumer
electronics. Table of Contents: Acknowledgments / Introduction /
Analysis of Rain / Dataset and Performance Metrics / Important Rain
Detection Algorithms / Probabilistic Approach for Detection and
Removal of Rain / Impact of Camera Motion on Detection of Rain /
Meteorological Approach for Detection and Removal of Rain from
Videos / Conclusion and Scope of Future Work / Bibliography /
Authors' Biographies
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