A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images

TL;DR

This paper reviews recent edge-preserving smoothing algorithms for digital images, discussing their mathematical foundations, applications, implementation strategies, and potential for hardware acceleration.

Contribution

It provides a comprehensive overview of edge-preserving filters, from classical to modern methods, highlighting their applications, mathematical analysis, and implementation considerations.

Findings

Edge-preserving filters effectively reduce noise while maintaining image edges.

Various algorithms like bilateral and guided filters are analyzed for efficiency and effectiveness.

The paper discusses potential hardware implementations for real-time processing.

Abstract

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.