Noise Invalidation Denoising

TL;DR

The paper introduces Noise Invalidation Denoising (NIDe), a versatile method that adaptively removes noise without assuming specific signal structures, outperforming existing methods in MSE.

Contribution

It presents a novel noise invalidation approach that does not rely on signal sparsity or smoothness assumptions, applicable in any basis.

Findings

NIDe outperforms existing denoising methods in MSE.

The method is effective across various basis transformations.

Experimental results validate the approach's robustness.

Abstract

A denoising technique based on noise invalidation is proposed. The adaptive approach derives a noise signature from the noise order statistics and utilizes the signature to denoise the data. The novelty of this approach is in presenting a general-purpose denoising in the sense that it does not need to employ any particular assumption on the structure of the noise-free signal, such as data smoothness or sparsity of the coefficients. An advantage of the method is in denoising the corrupted data in any complete basis transformation (orthogonal or non-orthogonal). Experimental results show that the proposed method, called Noise Invalidation Denoising (NIDe), outperforms existing denoising approaches in terms of Mean Square Error (MSE).