Combating Bad Weather Part I
Springer Nature
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…
