Adaptive-neighborhood histogram equalization for image enhancement |
| |
| Affiliation: | 1. Department of Orthodontics and Dentofacial Orthopaedics, Yenepoya Dental College, Yenepoya University, Deralakatte, Mangalore, India;2. Department of Orthodontics and Dentofacial Orthopedics, Modern Dental College & Research Centre, Indore, India;3. Department of Orthodontics and Craniofacial Biology, Radboud University Medical Center, Nijmegen, The Netherlands;1. Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, New Brunswick, E3B 5A3 Canada;2. Department of Chemistry and Centre for Laser, Atomic and Molecular Sciences, University of New Brunswick, Fredericton, New Brunswick E3B 5A3, Canada;1. School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan;2. Department of Oral and Maxillofacial Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan;3. Department of Orthodontics, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan;1. Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John''s, NL, A1C5S7, Canada;2. Departamento de Matemáticas e Instituto Universitario de Matemáticas y Aplicaciones, Universidad de Zaragoza, 50009 Zaragoza, Spain |
| |
| Abstract: | By modifying the histogram of an image, a dramatic improvement in the perceptibility of details can often be achieved. However, the two commonly used methods of full-frame histogram equalization and local-area histogram equalization often fail to produce adequate enhancement when the image contains relatively small but variable-sized regions in which there are objects or features of interest with low visual contrast. A new method of adaptive-neighborhood histogram equalization that is effective in enhancing these types of images is proposed in this paper. In this method, an adaptive neighborhood is developed for each pixel in the image. The adaptive neighborhood is a compound region made up of a foreground that contains 8-connected pixels close in gray level to that of the seed pixel, and a background of neighboring pixels molded around the foreground. The histogram of this adaptive neighborhood is equalized to provide the transformation that is applied to the seed pixel. Major advantages of this method are the avoidance of block edge artifacts that are encountered in local-area histogram equalization, and improved perceptibility of image detail. Examples of images transformed using the three methods of histogram modification are presented along with a discussion of the merits of the adaptive-neighborhood method. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
