Matlab Project with Source Code Image Enhancement Using Histogram Equalization and Bi-histogram Equalization

ABSTRACT

               Image enhancement is one of the challenging issues in low level image processing. Contrast enhancement techniques are used for improving visual quality of low contrast images. Histogram Equalization (HE) method is one such technique used for contrast enhancement. It is a contrast enhancement technique with the objective to obtain a new enhanced image with a uniform histogram. In this paper, instead of using conventional image enhancement techniques, we proposed a method called genetic algorithm for the enhancement of images. This algorithm is fast and very less time consuming as compared to other techniques such as global histogram equalization by taking CDF and finding out the transfer function. Here in our work we are going to enhance images using histogram equalization of images by re-configuring their pixel spacing using optimization through GA (Genetic algorithm). We will get more optimized results with the use of GA with respect to other optimization techniques.

                Digital image enhancement is one of the most important image processing technology which is necessary to improve the visual appearance of the image or to provide a better transform representation for future automated image processing such as image analysis, detection, segmentation and recognition. Many images have very low dynamic range of the intensity values due to insufficient illumination and therefore need to be processed before being displayed. Large number of techniques have focused on the enhancement of gray level images in the spatial domain. These methods include histogram equalization, gamma correction, high pass filtering, low pass filtering, homomorphic filtering, etc. Image enhancement techniques are of particular interest in photography, satellite imagery, medical applications and display devices. Producing visually natural is required for many important areas such as vision, remote sensing, dynamic scene analysis, autonomous navigation, and biomedical image analysis.

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