An HVS-directed neural-network-based image resolution enhancement scheme for image resizing

Chin Teng Lin, Kang Wei Fan, Her Chang Pu, Shih Mao Lu, Sheng Fu Liang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)605-615
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume15
Issue number4
DOIs
Publication statusPublished - 2007 Aug 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'An HVS-directed neural-network-based image resolution enhancement scheme for image resizing'. Together they form a unique fingerprint.

Cite this