Observer-dependent image enhancement

In many image-processing applications the image quality should be improved to support the human perception. Image quality evaluation by human observers is, however, heavily subjective in nature. Individual observers judge the image quality differently. In many cases, the quality of the relevant part of image information, which is perceived by the observer, should reach a maximum. In previous works, an overall system for subjective image enhancement, which is based on fusion of different algorithms, was introduced. In this paper, more details of the overall-system structure are provided. Furthermore, the test results for contrast and sharpness/smoothness as interesting image qualities are also presented.

[1]  W. Chase,et al.  Visual information processing. , 1974 .

[2]  Arthur R. Weeks.  Fundamentals of electronic image processing , 1996, SPIE/IEEE series on imaging science and engineering.

[3]  Allen Newell,et al.  Production Systems: Models of Control Structures , 1973 .

[4]  W. Hendee.  The Perception of Visual Information , 1993, Springer New York.

[5]  Hamid R. Tizhoosh,et al.  Fuzzy Image Processing , 2000, Computer Vision and Applications.

[6]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[7]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[8]  Yoshiaki Shirai.  3D computer vision and applications , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[9]  G. C. Higgins,et al.  The Nature and Evaluation of the Sharpness of Photographic Images , 1952 .

[10]  P. Gader,et al.  Advances in fuzzy integration for pattern recognition , 1994, CVPR 1994.

[11]  Bernd Reusch.  Computational Intelligence, Theory and Applications , 1997 .

[12]  Hamid R. Tizhoosh,et al.  Image enhancement based on fuzzy aggregation techniques , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[13]  Madan M. Gupta,et al.  Fuzzy automata and decision processes , 1977 .

[14]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[15]  A. Kandel,et al.  The use of weighted fuzzy expected value (WFEV) in fuzzy expert systems , 1989 .

[16]  Hamid R. Tizhoosh,et al.  Enhancement of Megavoltage Images in Radiation Therapy Using Fuzzy and Neural Image Processing Techniques , 2000 .

[17]  G. Klir,et al.  Fuzzy Measure Theory , 1993 .

[18]  J. Kacprzyk,et al.  Aggregation and Fusion of Imperfect Information , 2001 .

[19]  James M. Keller,et al.  The fusion of information via fuzzy integration , 1992 .

[20]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[21]  M. Sugeno.  FUZZY MEASURES AND FUZZY INTEGRALS—A SURVEY , 1993 .

[22]  Rangaraj M. Rangayyan,et al.  Subjective and objective evaluation of image sharpness: behavior of the region-based image edge profile acutance measure , 1996, Medical Imaging.

[23]  Gert Hauske.  Systemtheorie der visuellen Wahrnehmung , 1994 .

[24]  Qing Zhu,et al.  Fuzzy and Evidence Reasoning , 1995 .

[25]  L. Zadeh.  A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges , 1972 .

[26]  Hamid R. Tizhoosh,et al.  Locally Adaptive Fuzzy Image Enhancement , 1997, Fuzzy Days.

[27]  B. Michaelis,et al.  Subjectivity, psychology and fuzzy techniques: a new approach to image enhancement , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).