  Edge preserving filters preserve the edges and its information while blurring
an image. In other words they are used to smooth an image, while reducing the
edge blurring effects across the edge like halos, phantom etc. They are
nonlinear in nature. Examples are bilateral filter, anisotropic diffusion
filter, guided filter, trilateral filter etc. Hence these family of filters are
very useful in reducing the noise in an image making it very demanding in
computer vision and computational photography applications like denoising,
video abstraction, demosaicing, optical-flow estimation, stereo matching, tone
mapping, style transfer, relighting etc. This paper provides a concrete
introduction to edge preserving filters starting from the heat diffusion
equation in olden to recent eras, an overview of its numerous applications, as
well as mathematical analysis, various efficient and optimized ways of
implementation and their interrelationships, keeping focus on preserving the
boundaries, spikes and canyons in presence of noise. Furthermore it provides a
realistic notion for efficient implementation with a research scope for
hardware realization for further acceleration.
