Improved BM3D image denoising using SSIM-optimized Wiener filter

Mahmud Hasan,Mahmoud R El-Sakka

doi:10.1186/s13640-018-0264-z

Abstract

Image denoising is considered a salient pre-processing step in sophisticated imaging applications. Over the decades, numerous studies have been conducted in denoising. Recently proposed Block matching and 3D (BM3D) filtering added a new dimension to the study of denoising. BM3D is the current state-of-the-art of denoising and is capable of achieving better denoising as compared to any other existing method. However, there is room to improve BM3D to achieve high-quality denoising. In this study, to improve BM3D, we first attempted to improve the Wiener filter (the core of BM3D) by maximizing the structural similarity (SSIM) between the true and the estimated image, instead of minimizing the mean square error (MSE) between them. Moreover, for the DC-only BM3D profile, we introduced a 3D zigzag thresholding. Experimental results demonstrate that regardless of the type of the image, our proposed method achieves better denoising performance than that of BM3D.

Highlights

There are different types of noise that can contaminate a digital image
6.4 Performance analysis of DC-only profile Figure 8 shows a comparison between the performance of the proposed zigzag thresholded result and that of Block matching and 3D (BM3D) for DC-only profile, where Fig. 8a is the original image, Fig. 8b is the noisy image (σ = 20), and Fig. 8c, d are the output produced by BM3D (DC-only) and the proposed method, respectively
We proposed an improved Wiener filter optimized for structural similarity (SSIM) that has essentially improved the performance of BM3D

Summary

Introduction

There are different types of noise that can contaminate a digital image. Depending on the noise type, there are various algorithms present in the literature for denoising the image. Block matching and 3D (BM3D) filtering [5] is one such popular algorithm that reduces additive white Gaussian noise (AWGN) [16] from digital images. In terms of denoising performance, BM3D is considered the best denoising filter to date. It generates a basic estimate of the noisy image using hard thresholding. In the second step, it uses Wiener filter to denoise the noisy image. BM3D uses the basic estimate generated from the first step as an oracle (i.e., a pilot signal) in the Wiener filter

Objectives

Results

Discussion

Conclusion