A nonlinear Stein based estimator for multichannel image denoising

TL;DR

This paper introduces a nonlinear Stein-based estimator for multichannel image denoising that leverages multiscale representations and multivariate statistics to improve noise reduction in multisensor images.

Contribution

It proposes a novel parametric nonlinear estimator using Stein's principle for multivariate data, enhancing denoising performance over existing wavelet methods.

Findings

Outperforms conventional wavelet denoising techniques

Effective in multispectral remote sensing images

Utilizes multivariate statistical modeling

Abstract

The use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions. However, the observed images are often corrupted by an additive Gaussian noise. In this paper, we are interested in multichannel image denoising based on a multiscale representation of the images. A multivariate statistical approach is adopted to take into account both the spatial and the inter-component correlations existing between the different wavelet subbands. More precisely, we propose a new parametric nonlinear estimator which generalizes many reported denoising methods. The derivation of the optimal parameters is achieved by applying Stein's principle in the multivariate case. Experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising techniques