ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login


Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.4, No. 11)

Publication Date:

Authors : ; ;

Page : 400-408

Keywords : Histogram; Image Contrast; Image Enhancement; Histogram Equalization;

Source : Downloadexternal Find it from : Google Scholarexternal


Image enhancement methods are widely used for improving the feature and quality of the images. Image enhancement is to improve the visual appearance of an image or modify attributes of an image to make it more suitable for a specific application. Using local enhancement technique mean brightness of an image may loss and hence high computational time for enhancing the image. These limitations can be overcome by contrast enhancement. In Image enhancement, image contrast enhancement brings out hidden features of an image. Contrast enhancement changing the pixels intensity of the input image to utilize maximum possible bins. We need to study and review different image contrast enhancement performance measuring techniques because contrast losses the brightness in enhancement of image. In this research we did quantification of contrast level for many general and biomedical images. We considered two novel methods under evaluation: first metric is Histogram Flatness Measure (HFM) and second metric is Histogram Spread (HS). Simulation results are done extensively on various images and we found that HS is more reasonable than HFM. We found that even low contrast images are having high value of HFM in some images than original images instead of having low value of HFM, being inconsistent. But in case of HS, for all images HS value is low for low contrast, while for high contrast images HS value is higher than original images; which found to be consistent. So we found HS to be highly useful for standardizing the notion “low contrast low value; high contrast high value”; and HS is highly useful to distinguish between images of different contrast level. The accuracy of the metric is also verified for general and biomedical images. The standardization of the consistency for HS; is having high usefulness in image database management, visualization, image classification.

Last modified: 2015-11-17 12:42:32