Non-linear aggregation of filters to improve image denoising

04/01/2019
by   Benjamin Guedj, et al.
0

We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. The numerical performance of the method is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.

READ FULL TEXT

page 4

page 6

page 7

page 8

page 9

page 10

page 11

research
09/14/2019

Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction

During the acquisition of an image from its source, noise always becomes...
research
02/11/2015

Image denoising based on improved data-driven sparse representation

Sparse representation of images under certain transform domain has been ...
research
10/20/2017

Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field

In this paper, we consider Bayesian image denoising based on a Gaussian ...
research
10/13/2018

No-reference Image Denoising Quality Assessment

A wide variety of image denoising methods are available now. However, th...
research
06/27/2014

On a new formulation of nonlocal image filters involving the relative rearrangement

Nonlocal filters are simple and powerful techniques for image denoising....
research
07/06/2010

Bilateral filters: what they can and cannot do

Nonlinear bilateral filters (BF) deliver a fine blend of computational s...
research
11/09/2013

Neighborhood filters and the decreasing rearrangement

Nonlocal filters are simple and powerful techniques for image denoising....

Please sign up or login with your details

Forgot password? Click here to reset