Monday, June 28, 2004



1. School of Engineering & Technology, Bharathidasan University, Trichy.
J.J. College of Engineering & Technology, Trichy.

The term ‘Digital Image Processing’ generally refers to processing of a two-dimensional picture by a digital computer. A digital image is an array of real or complex numbers represented by a fine number of bits. Image processing field deals with the improvement of images for human perception. Images can have either analog or digital representation.

One of the various fields in Image Processing is ‘Image Enhancement’. Image enhancement consists of a collection of techniques that seek to improve the visual appearance of an image or to convert the image into a form better suited for analysis by a human or machine.

Image Representation : The digital image is represented as a two-dimensional array of numbers. In other words, the digital image can be considered a matrix whose row and column indices identify a point in the image and the corresponding matrix element value identifies the gray level at that point. The elements of such a digital array are called ‘Image elements’ orPicture elements’ or ‘pixels’ or ‘pets’.

If each gray level is represented as 8 bits, then there are 28 or 256 possible gray levels. These levels are usually assigned integer values ranging from 0 to 255 with 0- representing the darkest intensity level, and 255 the brightest intensity level.

Image Enhancement : Image enhancement is the basic tool used to improve the visual appearance of images for human perception. Image enhancement is useful in feature extraction, image analysis and visual information display. Image Restoration: Image restoration refers to removal or minimization of known degradations in an image. This includes de-blurring of images degraded by the limitations of a sensor or its noise filtering and correction of geometric distortion.
Image Analysis : Image analysis is concerned with making quantitative measurements from an image to produce the description of it. Image Reconstruction. : Image reconstruction from projections is a special class of image restoration problems where a two or higher dimensional object is reconstructed from several one-dimensional projections. Image Data Compression: Image data compression techniques are concerned with reduction of the number of bits required to store or transmit images without any appreciable loss of information.

Image enhancement remains a very important topic because of its usefulness in all image-processing applications. The enhancement process does not increase the inherent information content in the data. But it does increase the dynamic range of the chosen feature so that they can be detected easily.

One of the main drawbacks of histogram equalization is that the brightness of an image is changed after equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving original brightness is necessary in order not to introduce any unnecessary visual detoriation. A darker image becomes much brighter and vice versa. In short, the histogram equalization does not take the mean brightness of an image into account.

The ultimate goal of the bi-histogram equalization algorithm is to preserve the mean brightness of a given image while the contrast is enhanced. Brightness Preserving Bi-Histogram Equalization (BBHE) is the novel extension of histogram equalization. By this algorithm, the contrast of the image is enhanced without changing the mean brightness of input image.

Brightness preserving bi-histogram equalization (BBHE) firstly decomposes an input image into two-sub images based on the mean of the input image. One of the sub-images is the set of samples less than or equal to the mean, whereas the other one is the set of samples greater than the mean. Then the BBHE equalize the sub-images independently based on their respective histograms with a constraint that the samples in the formal set is mapped into the range from minimum gray level to the input mean and the samples in the latter set are mapped into the range from mean to maximum gray level. In other words, one of the sub-images is equalized over the range up to the mean, and the other sub-image is equalized over the range from the mean based on the respective histograms. Thus, the resulting equalized sub-images are bound by each other around the input mean, which has an effect of preserving the mean brightness.

Thorough understanding of this article makes anyone to visualize the better enhancement by bi-histogram equalization. It is a newly-developed contrast enhancement algorithm. Based on this algorithm, it is clear that the brightness preserving bi-histogram equalization is capable of preserving the mean brightness of the given image while enhancing the contrast.


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