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.
