Boosting of Denoising Effect with Fusion Strategy

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.

A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation

“…Additionally, a histogram equalization term was applied to image contrast enhancement, and both image structure and texture were retained by a fidelity term. Yang [44] used both non-locally centralized sparse representation (NCSR) and residual learning of deep CNN (DnCNN) to achieve internal and external denoising, respectively. The simultaneous image denoising and fusion was converted to an adaptive weight-based image fusion of the denoised image details obtained by NCSR and DnCNN.…”