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

Authors:
Chin-Teng Lin;Kang-Wei Fan;Her-Chang Pu;Shih-Mao Lu;Sheng-Fu Liang
Affiliations:
Nat. Chiao-Tung Univ., Hsinchu;-;-;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2007

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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.