Multiscale super-resolution reconstruction via multibranch prediction and selection network

Wei Wang; Fei Wang; Zhiliang Qiu; Ruizhi Jin

doi:10.1117/1.JEI.27.4.043007

9 July 2018 Multiscale super-resolution reconstruction via multibranch prediction and selection network

Wei Wang, Fei Wang, Zhiliang Qiu, Ruizhi Jin

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043007 (July 2018). https://doi.org/10.1117/1.JEI.27.4.043007

Abstract

Convolutional neural networks have been recently shown to have the highest accuracy for single-image super-resolution reconstruction. A multibranch prediction and selection network that can gradually reconstruct robust images in multiple scales is proposed. This endeavor is achieved through a network structure with two interacting subnetworks: one is a deep cascaded, multibranch prediction network (DCMBPN) and another is a deep block-selection network (DBSN). In particular, in each cascade, DCMBPN predicts multiple reconstructed images progressively with its special multibranch and cascaded structure. DBSN then adaptively selects the predicted confident blocks from these reconstructed images. Our method does not require traditional interpolation methods to upsample the image as a preprocessing step. It, thus, greatly reduces the computational complexity. We use Euclidean and perception loss functions in each branch to obtain two high-quality reconstructions. In addition, for the cascade structure, our network can achieve reconstructions in different scales, such as 1.5 × , 2 × , 2.5 × , 3 × , 3.5 × , and 4 × . Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and visual improvement.

Citation Download Citation

Wei Wang, Fei Wang, Zhiliang Qiu, and Ruizhi Jin "Multiscale super-resolution reconstruction via multibranch prediction and selection network," Journal of Electronic Imaging 27(4), 043007 (9 July 2018). https://doi.org/10.1117/1.JEI.27.4.043007

Received: 9 March 2018; Accepted: 13 June 2018; Published: 9 July 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

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Super resolution

Convolution

Lawrencium

Feature extraction

Reconstruction algorithms

Data modeling

Networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years