Quelques traitements bas niveau basés sur une analyse du contraste local

In this contribution, a simple method of low level vision treatments based on local contrast analysis is proposed. It is shown that many local treatments could be done with the use of only one quantity : the mean-edge value estimated in a local sliding window. This basic idea has been successfully used for contrast enhancement in a previous work. The treatments offered in the present method are : contrast enhancement, smoothing, dynamic greylevel thresholding and contour detection. The four treatments are evaluated on synthetic and real images.

[1]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[2]  Azeddine Beghdadi,et al.  An image enhancement technique and its evaluation through bimodality analysis , 1992, CVGIP Graph. Model. Image Process..

[3]  Olav Lillesaeter,et al.  Complex contrast, a definition for structured targets and backgrounds , 1993 .

[4]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[5]  Sankar K. Pal,et al.  Thresholding for edge detection using human psychovisual phenomena , 1986, Pattern Recognit. Lett..

[6]  F. Kammoun,et al.  Augmentation de contraste «adaptative» suivant les niveaux de gris des contours , 1990 .

[7]  A. Mokrane,et al.  A new image contrast enhancement technique based on a contrast discrimination model , 1992, CVGIP Graph. Model. Image Process..

[8]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  Parry Moon,et al.  THE VISUAL EFFECT OF NON-UNIFORM SURROUNDS: , 1945 .

[10]  Rangaraj M. Rangayyan,et al.  Region-based contrast enhancement of mammograms , 1992, IEEE Trans. Medical Imaging.

[11]  R. Haber,et al.  Visual Perception , 2018, Encyclopedia of Database Systems.

[12]  Azeddine Beghdadi,et al.  Edge detection using Holladay's principle , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[13]  Azriel Rosenfeld,et al.  Threshold Evaluation Techniques , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[15]  J.-M. Jolion,et al.  Analyse multirésolution du contraste dans les images numériques , 1994 .

[16]  Azeddine Beghdadi,et al.  Contrast enhancement technique based on local detection of edges , 1989, Comput. Vis. Graph. Image Process..

[17]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  B. N. Chatterji,et al.  Adaptive contrast enhancement and de-enhancement , 1991, Pattern Recognit..

[19]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[20]  R M Rangayyan,et al.  Feature enhancement of film mammograms using fixed and adaptive neighborhoods. , 1984, Applied optics.

[21]  Debargha Mukherjee,et al.  Adaptive Neighborhood Extended Contrast Enhancement and Its Modifications , 1995, CVGIP Graph. Model. Image Process..

[22]  D. Bradley.  Modern Optics , 1968, Nature.

[23]  Robert A. Hummel,et al.  Image Enhancement by Histogram transformation , 1975 .

[24]  Sanjit K. Mitra,et al.  New adaptive image contrast enhancement method , 1993, Electronic Imaging.

[25]  Werner Frei,et al.  Image Enhancement by Histogram Hyperbolization , 1977 .

[26]  R. Gordon,et al.  Enhancement of Mammographic Features by Optimal Adaptive Neighborhood Image Processing , 1986, IEEE Transactions on Medical Imaging.