Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation

TL;DR

This paper introduces a novel underwater image denoising method using discrete wavelet transform with multi-level noise estimation, effectively reducing colored noise and improving image quality as measured by PSNR.

Contribution

It proposes a new multi-level noise estimation technique in discrete wavelet transform tailored for underwater colored noise, enhancing denoising performance over existing methods.

Findings

Wavelet-based denoising improves PSNR and image clarity.

Different wavelet bases affect denoising effectiveness.

The method outperforms traditional denoising techniques.

Abstract

In many applications, Image de-noising and improvement represent essential processes in presence of colored noise such that in underwater. Power spectral density of the noise is changeable within a definite frequency range, and autocorrelation noise function is does not like delta function. So, noise in underwater is characterized as colored noise. In this paper, a novel image de-noising method is proposed using multi-level noise power estimation in discrete wavelet transform with different basis functions. Peak signal to noise ratio (PSNR) and mean squared error represented performance measures that the results of this study depend on it. The results of various bases of wavelet such as: Daubechies (db), biorthogonal (bior.) and symlet (sym.), show that denoising process that uses in this method produces extra prominent images and improved values of PSNR than other methods.