Boosting of Denoising Effect with Fusion Strategy

Fangjia Yang,Chongxi Li,Shaoping Xu

doi:10.3390/app10113857

Abstract

Image denoising, a fundamental step in image processing, has been widely studied for several decades. Denoising methods can be classified as internal or external depending on whether they exploit the internal prior or the external noisy-clean image priors to reconstruct a latent image. Typically, these two kinds of methods have their respective merits and demerits. Using a single denoising model to improve existing methods remains a challenge. In this paper, we propose a method for boosting the denoising effect via the image fusion strategy. This study aims to boost the performance of two typical denoising methods, the nonlocally centralized sparse representation (NCSR) and residual learning of deep CNN (DnCNN). These two methods have complementary strengths and can be chosen to represent internal and external denoising methods, respectively. The boosting process is formulated as an adaptive weight-based image fusion problem by preserving the details for the initial denoised images output by the NCSR and the DnCNN. Specifically, we design two kinds of weights to adaptively reflect the influence of the pixel intensity changes and the global gradient of the initial denoised images. A linear combination of these two kinds of weights determines the final weight. The initial denoised images are integrated into the fusion framework to achieve our denoising results. Extensive experiments show that the proposed method significantly outperforms the NCSR and the DnCNN both quantitatively and visually when they are considered as individual methods; similarly, it outperforms several other state-of-the-art denoising methods.

Highlights

Digital images are often corrupted by noise during the acquisition or transmission of the images [1], rendering these images unsuitable for vision applications such as remote sensing and object recognition.image denoising is a fundamental preprocessing step that aims at suppressing noise and reproducing the latent high quality image with fine image edges, textures, and rich details
We introduce a denoising effect boosting method based on an image fusion strategy
We introduce a denoising effect boosting method to improve the denoising performance of a single method, nonlocally centralized sparse representation (NCSR) or DnCNN

Summary

Introduction

Digital images are often corrupted by noise during the acquisition or transmission of the images [1], rendering these images unsuitable for vision applications such as remote sensing and object recognition.image denoising is a fundamental preprocessing step that aims at suppressing noise and reproducing the latent high quality image with fine image edges, textures, and rich details. Digital images are often corrupted by noise during the acquisition or transmission of the images [1], rendering these images unsuitable for vision applications such as remote sensing and object recognition. A corrupted noisy image can be generally described as: y = x+v (1). Where the column vector x denotes the original clean image, and the v denotes the additive noise. There are many possible solutions for x of a noisy image y because the noise v is unknown. This is a fact that encourages scholars to continue seeking for new methods to achieve better denoising results. Various image denoising studies assume v to be additive white gaussian noise (AWGN). Considering that AWGN is stationary and uncorrelated among pixels, we made the same assumption for this study

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