A multiscale feature extraction feedback network for single image super-resolution

Dezhi Yang; Qingsong Xie; Jinjie Xiao

doi:10.1117/12.3051876

18 November 2024 A multiscale feature extraction feedback network for single image super-resolution

Dezhi Yang, Qingsong Xie, Jinjie Xiao

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340329 (2024) https://doi.org/10.1117/12.3051876
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In conventional single image super-resolution (SISR) models utilizing deep learning, information is generally transmitted in a feedforward fashion. Nonetheless, these models have not fully exploited the feedback mechanism. Drawing inspiration from the feedback process in human vision, we introduce a Multiscale Feature Extraction Feedback Network (MFEFN), which utilizes high-level information to enhance low-level details. In particular, we employ the principles of Recurrent Neural Networks (RNNs) to reintroduce part of the output back into the model as input, thereby optimizing the input feature representations. We design a multiscale feature extraction channel attention feedback module to handle the feedback connections and generate rich detail texture features. Additionally, to address the issue of limited receptive fields and singular convolutional kernels in models, we propose a multiscale feature extraction module to capture detail texture features at different scales. To further enhance the model's cross-channel learning capability, we incorporate a channel attention mechanism. Comprehensive experimental results showcase the proposed MFEFN's superiority over current state-of-the- art methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dezhi Yang, Qingsong Xie, and Jinjie Xiao "A multiscale feature extraction feedback network for single image super-resolution", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340329 (18 November 2024); https://doi.org/10.1117/12.3051876

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Feature extraction

Super resolution

Deconvolution

Convolution

Visual process modeling

Lawrencium

Performance modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years