Title: An HVS-Directed neural-network-based image resolution enhancement scheme for image resizing
Authors: Lin, Chin-Teng
Fan, Kang-Wei
Pu, Her-Chang
Lu, Shih-Mao
Liang, Sheng-Fu
交大名義發表
生物科技學系
資訊工程學系
電控工程研究所
National Chiao Tung University
Department of Biological Science and Technology
Department of Computer Science
Institute of Electrical and Control Engineering
Keywords: fuzzy decision system;human visual system;image interpolation;neural network;resolution enhancement
Issue Date: 1-Aug-2007
Abstract: In this paper, a novel human visual system (HVS)directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.
URI: http://dx.doi.org/10.1109/TFUZZ.2006.889875
http://hdl.handle.net/11536/10505
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2006.889875
Journal: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 15
Issue: 4
Begin Page: 605
End Page: 615
Appears in Collections:Articles


Files in This Item:

  1. 000248703700007.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.