A super-resolution reconstruction algorithm of infrared pedestrian images via compressed sensing

Erbo Zou; Bo Lei; Nan Jing; Hai Tan

doi:10.1117/12.2502484

25 October 2018 A super-resolution reconstruction algorithm of infrared pedestrian images via compressed sensing

Erbo Zou, Bo Lei, Nan Jing, Hai Tan

Proceedings Volume 10822, Real-time Photonic Measurements, Data Management, and Processing III; 108220V (2018) https://doi.org/10.1117/12.2502484
Event: SPIE/COS Photonics Asia, 2018, Beijing, China

Abstract

Pedestrian detection is the major task of many infrared surveillance system. Due to the technical limitation of sensor or the high cost of advanced hardware, the resolution of infrared images is usually low, which is not capable of meeting the high quality requirement of various applications. Compressed sensing capturing and represents compressible signals at a sample rate significantly below the Nyquist rate, is considered as a new framework for signal reconstruction based on the sparsity and compressibility. Thus, the compressed sensing theory enlightens a computational way to reconstruct a high resolution image on the basis of a sparse signal, i.e. the low resolution image. The proposed method use low resolution and high resolution infrared pedestrian images to train an over-complete dictionary through K-SVD algorithm, by which the pedestrian are sparsely well-represented. Two distant infrared cameras in the same scene are used to capture high and low resolution image to make sure same pedestrian pair is sparsely represented under the over-complete dictionary. Therefore the similarities are learning between input low resolution image patches and high resolution image patches. The popular greedy algorithm Orthogonal Matching Pursuit (OMP) is utilized for sparse reconstruction, providing optimal performance and guaranteeing less computational cost and storage. We evaluate the quality of reconstructed image employing root mean square error and peak signal to noise. The experimental results show that the reconstructed images preserve wealthy detailed information of pedestrian, and have low RMSE and high PSNR, which are superior to the traditional super-resolution methodologies.

Citation Download Citation

Erbo Zou, Bo Lei, Nan Jing, and Hai Tan "A super-resolution reconstruction algorithm of infrared pedestrian images via compressed sensing", Proc. SPIE 10822, Real-time Photonic Measurements, Data Management, and Processing III, 108220V (25 October 2018); https://doi.org/10.1117/12.2502484

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
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image resolution

Reconstruction algorithms

Infrared imaging

Infrared radiation

Associative arrays

Super resolution

Infrared sensors

Show All Keywords

Keywords/Phrases

Search In:

Publication Years