A novel neural-network-based image resolution enhancement

Her Chang Pu, Chin Teng Lin, Sheng Fu Liang, Nimit Kumar

Research output: Contribution to conferencePaper

2 Citations (Scopus)


In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception non-sensitive class and sensitive class. 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 higher visual quality of the interpolated image than the conventional interpolation methods.

Original languageEnglish
Number of pages6
Publication statusPublished - 2003 Jul 11
EventThe IEEE International conference on Fuzzy Systems - St. Louis, MO, United States
Duration: 2003 May 252003 May 28


OtherThe IEEE International conference on Fuzzy Systems
CountryUnited States
CitySt. Louis, MO


All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Pu, H. C., Lin, C. T., Liang, S. F., & Kumar, N. (2003). A novel neural-network-based image resolution enhancement. 1428-1433. Paper presented at The IEEE International conference on Fuzzy Systems, St. Louis, MO, United States.