Multiscale parallel feature extraction convolution neural network for image denoising

Xiaofen Jia; Huarong Chai; Yongcun Guo; Yourui Huang; Baiting Zhao

doi:10.1117/1.JEI.27.6.063031

20 December 2018 Multiscale parallel feature extraction convolution neural network for image denoising

Xiaofen Jia, Huarong Chai, Yongcun Guo, Yourui Huang, Baiting Zhao

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063031 (December 2018). https://doi.org/10.1117/1.JEI.27.6.063031

Abstract

Image denoising based on a convolution neural network (CNN) can be described as the problem of learning a mapping function from a noisy image to a clean image through an end-to-end training. We propose a multiscale parallel feature extraction module (MPFE) for CNN denoising, which integrates residual learning and dense connection. The MPFE uses convolution kernels of different sizes to adaptively extract multiple features in different scales from the input image. We introduce dense connection to connect each MPFE, which can make different features interact with each other and concatenate together, so as to fully exploit the image features. The dense connection can pass the features that carry many image details, which help reduce image distortion. Meanwhile, it can also reduce gradient disappearance and improve convergence speed. The MPFE uses residual learning to resolve the gradient loss caused by high network depth while still ensuring that the network learns the details of the noisy image. Simulation experiments show that our denoising method has the ability of suppressing Gaussian noises with different noise levels, it performs superior performance over the state-of-the-art denoising methods.

Citation Download Citation

Xiaofen Jia, Huarong Chai, Yongcun Guo, Yourui Huang, and Baiting Zhao "Multiscale parallel feature extraction convolution neural network for image denoising," Journal of Electronic Imaging 27(6), 063031 (20 December 2018). https://doi.org/10.1117/1.JEI.27.6.063031

Received: 23 August 2018; Accepted: 30 November 2018; Published: 20 December 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