ABSTRACT
Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. Histogram equalization is a well-known method for enhancing the contrast of a given image in accordance with the sample distribution. In general, histogram equalization flattens the density distribution of the resultant image and enhances the contrast of the image as a consequence. In spite of its high performance in enhancing contrasts of a given image, however, Global histogram equalization may change the original brightness of an input image, deteriorate visual quality, or, introduce some annoying artifacts.
PROJECT OUTPUT
Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. Histogram equalization is a well-known method for enhancing the contrast of a given image in accordance with the sample distribution. In general, histogram equalization flattens the density distribution of the resultant image and enhances the contrast of the image as a consequence. In spite of its high performance in enhancing contrasts of a given image, however, Global histogram equalization may change the original brightness of an input image, deteriorate visual quality, or, introduce some annoying artifacts.
PROJECT OUTPUT
Contact:
Mr. Roshan P. Helonde
Mobile / WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
