An HVS-Directed Neural-Network-Based Image Resolution Enhancement Scheme for Image Resizing

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...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2007-08, Vol.15 (4), p.605-615
Main Authors: Lin, Chin-Teng, Fan, Kang-Wei, Pu, Her-Chang, Lu, Shih-Mao, Liang, Sheng-Fu
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Computer simulation
Digital images
Fuzzy decision system
Fuzzy systems
Human
human visual system
Humans
image interpolation
Image resolution
Interpolation
neural network
Neural networks
Pixel
resolution enhancement
Supervised learning
Trains
Visual
Visual system
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Summary: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.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2006.889875