Work

Adversarial Gaussian Denoiser for Multiple-Level Image Denoising

Year: 2021

Type: article

Abstract: Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

Source: Sensors

DOI: 10.3390/s21092998

Authors: Aamir Khan, Weidong Jin, Amir Haider, MuhibUr Rahman, Desheng Wang

Institutions: Southwest Jiaotong University, Nanning University, Sejong University, Polytechnique Montréal

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,376

Funder: National Natural Science Foundation of China