abstract

Abstract

The main idea of the proposed algorithm: instead of evaluating all samples around a window (left), Ω, a subset of them, Λ, is used (centre), and their contributions with their respective weights are summed up obtaining the final value (right).

In this work we present a new algorithm for accelerating the colour bilateral filter based on a subsampling strategy working in the spatial domain. The base idea is to use a suitable subset of samples of the entire kernel in order to obtain a good estimation of the exact filter values. The main advantages of the proposed approach are that it has an excellent trade-off between visual quality and speed-up, a very low memory overhead is required and it is straightforward to implement on the GPU allowing real-time filtering. We show different applications of the proposed filter, in particular efficient cross-bilateral filtering, real-time edge-aware image editing and fast video denoising. We compare our method against the state of the art in terms of image quality, time performance and memory usage.

Errata Corrige: -Figure 2: Poisson-disk patterns (PDS) and Monte-Carlo Stratified (jiitering) patterns (SMS) should be swapped.

Interactive Demo in WebGL/SpiderGL using Importance Sampling FPS : 0

Range Sigma

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