Asee peer logo

Simplified Teaching And Understanding Of Histogram Equalization In Digital Image Processing

Download Paper |


2009 Annual Conference & Exposition


Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009



Conference Session

Pedagogy and Assessment I

Tagged Division

Electrical and Computer

Page Count


Page Numbers

14.1060.1 - 14.1060.20



Permanent URL

Download Count


Request a correction

Paper Authors


Shanmugalingam Easwaran Pacific Lutheran University

visit author page

Shanmugalingam Easwaran holds Ph.D., MS (Clemson University, SC), and BS (University of Peradeniya, Sri Lanka) degrees in Electrical Engineering. He is currently an Assistant Professor in the Computer Science and Computer Engineering department at Pacific Lutheran University (WA). Prior to this, he was an Assistant Professor at Xavier University of Louisiana (LA). Before joining the academia, he was in the industrial sector working for companies such as NYNEX Science and Technology, Periphonics Corporation, and 3Com Corporation. His teaching and research interests include areas such as Digital Signal, Speech, and Image Processing; Pattern Classification and Recognition; Digital and Analog Communications; and Digital and Embedded Systems and Microprocessors.

visit author page

Download Paper |

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Simplified Teaching and Understanding of Histogram Equalization in Digital Image Processing

1.0 Abstract

Histogram equalization is a widely used contrast-enhancement technique in image processing. This subtopic is included in almost all image-processing courses and textbooks. It is however one of the difficult image processing techniques to fully understand, especially for those encountering it for the first time. This is because of the complex nature of the mathematics used in standard and other textbooks in introducing histogram equalization. To alleviate these problems and to provide to those wanting to understand this topic with an insight into the process and operations involved, the author developed a simpler teaching/ learning framework and background (methodology), a simple and clear theory and the necessary derived equations, a clear histogram equalization process, and a MATLAB GUIDE® based GUI tool for visual demonstrations. Because of these developments, it was possible to easily explain and teach histogram equalization clearly at a very high level of rigor than was otherwise possible.

2.0 Introduction

Some images contain significant amounts of details which are many times not visually apparent, and thus these images may not be suitable for any serious visual analysis or even viewing pleasure. One reason for this may be is that these images are poorly contrasted, i.e., they have a poor dynamic range in their pixel intensities. What is desirable in such situations is that the dynamic range of these intensities (gray levels) in the images are made much higher, and thus provide improved visual contrast for greatest contrast clarity (meaning that their intensity distributions be made much more spread across the full intensity range). Because of this need, various contrast-enhancement techniques1-9 are applied to an image in such situations.

One subclass within such contrast-enhancement techniques is known as image contrast- stretching. This is a pixel (point) processing class of technique in which pixel intensities in an image are remapped to appropriate values based on a desired visual end result. A very important contrast-enhancement/ stretching technique with wide applications is an automatic procedure known as histogram equalization1-9. It is called an automatic procedure because it does not require any user control parameters for its application to an image.

Because of the importance of histogram equalization and its potential wide applications, this subtopic is included in almost all serious image-processing courses and textbooks1-9. However, it is one of the difficult image processing techniques to understand and implement especially for those encountering it for the first time (except when using a canned function to perform its operation). The reason for this difficulty is because, though an image in this regard has nothing to do with probabilities and probability distributions as such in general, the formulation and presentation of the background and theory for histogram equalization in almost all standard and other textbooks1-9 are based on the above “advanced” background and theory with the additional use of integral calculus further confusing and complicating the background and theory needed.

Easwaran, S. (2009, June), Simplified Teaching And Understanding Of Histogram Equalization In Digital Image Processing Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--5165

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2009 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015