Accelerated graph-based nonlinear denoising filters
This work addresses efficiency issues in image denoising for applications requiring fast processing, but it is incremental as it builds on existing filter methods.
The paper tackled the problem of slow convergence in graph-based nonlinear denoising filters by formulating acceleration techniques using conjugate gradient and Nesterov's methods, resulting in a 2-12 times speed-up in iterations to achieve a given PSNR.
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.