Dilated residual encode–decode networks for image denoising

Shengyu Li; Xuesong Liu; Rongxin Jiang; Fan Zhou; Yaowu Chen

doi:10.1117/1.JEI.27.6.063005

15 November 2018 Dilated residual encode–decode networks for image denoising

Shengyu Li, Xuesong Liu, Rongxin Jiang, Fan Zhou, Yaowu Chen

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063005 (November 2018). https://doi.org/10.1117/1.JEI.27.6.063005

Abstract

Owing to recent advancements, very deep convolutional neural networks (CNNs) have found application in image denoising. However, while deeper models lead to better restoration performance, they are marred by a high number of parameters and increased training difficulty. To address these issues, we propose a CNN-based framework, named dilated residual encode–decode networks (DRED-Net), for image denoising, which learns direct end-to-end mappings from corrupted images to obtain clean images using few parameters. Our proposed network consists of multiple layers of convolution and deconvolution operators; in addition, we use dilated convolutions to boost the performance of our network without increasing the depth of the model or its complexity. Extensive experiments on synthetic noisy images are conducted to evaluate DRED-Net, and the results are compared with those obtained using state-of-the-art denoising methods. Our experimental results show that DRED-Net leads to results comparable with those obtained using other state-of-the-art methods for image denoising tasks.

Citation Download Citation

Shengyu Li, Xuesong Liu, Rongxin Jiang, Fan Zhou, and Yaowu Chen "Dilated residual encode–decode networks for image denoising," Journal of Electronic Imaging 27(6), 063005 (15 November 2018). https://doi.org/10.1117/1.JEI.27.6.063005

Received: 4 June 2018; Accepted: 23 October 2018; Published: 15 November 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available