Image Enhancement using Recursive Separate Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization
Sandeepa K S1, Basavaraj N Jagadale2, J S Bhat3
1Sandeepa K S*, Department of electronics, Kuvempu University, Jnanashyadri, Shimoga, India.
2Basavaraj N Jagadale, Department of Electronics, Kuvempu University, Jnanasahyadri, Shimoga, India.
3J S Bhat, Indian Institute of Information Technology, Surat, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 782-787 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8515019320/2020©BEIESP | DOI: 10.35940/ijitee.C8515.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: To improve image contrast, this paper introduces a recursive separate standard intensity deviation based clipped sub image histogram equalization method. This is an extension of standard intensity deviation value based sub image histogram equalization algorithm, in terms of histogram separation and equalization. In existing equalization methods do not effectively utilizes the information from different region in equalization process. In this scheme, the image histogram is bisected based on standard intensity deviation value. The further separation is carried out based on the specific region threshold value and the resulting four sub histograms are equalized individually. This is an effective method for enhancing, low exposure, medical and mammogram images and for addressing the over-enhancement problem. The performance evaluation of the proposed method is presented with the help of average information and visual quality assessment and the proposed algorithm outperforms existing recursive algorithms based on histogram equalization.
Keywords: Histogram Equalization, Clipped Histogram, Standard Intensity Deviation Value, low Contrast Enhancement, Entropy Value.
Scope of the Article: Image analysis and processing