Gaussian kernel-based variable-grid image super-resolution reconstruction

Cheng Zhou; Yi-hua Tan; Jin-wen Tian; Wen-po Ma

doi:10.1117/12.831331

30 October 2009 Gaussian kernel-based variable-grid image super-resolution reconstruction

Cheng Zhou, Yi-hua Tan, Jin-wen Tian, Wen-po Ma

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749808 (2009) https://doi.org/10.1117/12.831331
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

A new method for super-resolution reconstruction based on the Gaussian-kernel is presented. Each pixel is modeled as a Gaussian distribution to reconstruct, which is iterated by the image weighting parameter adaptively. The parallelism of this real-valued algorithm based on the grid model enables better integration of the information of the low-resolution images of the same scene. Compared to the bi-cubic interpolation algorithm, experiments show that the proposed algorithm can achieve a gain up over 1.0dB. The visual quality of presented algorithm demonstrate the recovery of spatial frequencies above the band-limit and corresponding reduction in ringing artifacts when compared with the bicubic interpolation algorithm. And the proposed method gets better objective and subjective quality by preserving the sharpness of the edges.

Citation Download Citation

Cheng Zhou, Yi-hua Tan, Jin-wen Tian, and Wen-po Ma "Gaussian kernel-based variable-grid image super-resolution reconstruction", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749808 (30 October 2009); https://doi.org/10.1117/12.831331

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