A Matlab Guide® Based Gui Tool To Enhance Teaching And Understanding Of Histogram Matching In Digital Image Processing
Shanmugalingam Easwaran · 2020
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract A MATLAB GUIDE®-Based GUI Tool to Enhance Teaching and Understanding of Histogram Matching in Digital Image Processing 1.0 Abstract There are many instances when it is desirable and even necessary to modify an image to match its pixel intensity histogram to that of a target image. Because of the importance of this, histogram matching is included in almost all image-processing courses and textbooks as a subtopic. It is however one of the most 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 the standard and other textbooks in introducing histogram matching. The difficulty in understanding this subtopic is compounded by the fact that it also involves histogram equalization, which is another difficult to understand subtopic. In order to alleviate these problems and to provide satisfaction to those wanting to understand this subtopic 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 process for histogram matching, and a MATLAB GUIDE® based GUI tool for visual demonstrations. Because of these teaching developments, it was possible to easily and very clearly explain and teach histogram matching at a very high level of rigor than was otherwise possible. 2.0 Introduction Either to improve the dynamic range of pixel intensities in an image or to simply match the overall image intensities of one image to another for various purposes, there is a need to modify pixel intensities in an image to match its intensity histogram with that of a target image using a pixel intensity transformation technique. This technique is known as histogram matching. It is an automatic procedure in that it does not require any user control parameters for its application to an image. Because of the importance of histogram matching and its wide application potential, this subtopic is included in almost all image-processing courses and textbooks1-9. It is however one of the most difficult image processing techniques to fully understand and implement especially for those encountering it for the first time (except, may be, 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 matching 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. The understanding and learning of this subtopic is compounded by the fact that it also involves histogram equalization1-9 which is another difficult to understand subtopic discussed through the use of probability, probability distributions, integral calculus, etc.