Accelerated graph-based nonlinear denoising filters

TL;DR

This paper introduces two acceleration techniques, conjugate gradient and Nesterov's acceleration, to improve the efficiency of graph-based nonlinear denoising filters, achieving significant speed-ups in image denoising tasks.

Contribution

It presents novel acceleration methods for iterative graph-based denoising filters, reducing computation time while maintaining denoising quality.

Findings

Achieved 2-12 times speed-up in denoising iterations.

Demonstrated efficiency of accelerated filters in image denoising.

Reduced iterations needed for target PSNR.

Abstract

Denoising filters, such as bilateral, guided, and total variation filters, applied to images on general graphs may require repeated application if noise is not small enough. We formulate two acceleration techniques of the resulted iterations: conjugate gradient method and Nesterov's acceleration. We numerically show efficiency of the accelerated nonlinear filters for image denoising and demonstrate 2-12 times speed-up, i.e., the acceleration techniques reduce the number of iterations required to reach a given peak signal-to-noise ratio (PSNR) by the above indicated factor of 2-12.