Abstract
Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.
Highlights
Digital Image Processing (DIP) involves the modification of digital data for improving the image qualities with the aid of computer
The Proposed method is named as “Contrast enhancement and fuzzification histogram equalization (CEFHE)” in which a gray scale or colour image has been taken and passes through various steps to enhance the contrast of the given image
In this paper four techniques are used for image enhancement, which are CEFHE, Contrast Limited adaptive Histogram Equalization (CLAHE), Dualistic Sub Image Histogram Equalization (DSIHE) and Dynamic Histogram Equalization (DHE)
Summary
Digital Image Processing (DIP) involves the modification of digital data for improving the image qualities with the aid of computer. The processing helps in maximising clarity sharpness and details of features of interest towards information extraction and further analysis [1] This form of remote sensing began in 1960s with a limited number of researchers analysing airborne multispectral scanner data and digitised aerial photographs. The digital image is fed into a computer and computer is programmed to manipulate these data using an equation, or series of equations and store the results of the computation for each pixel (picture element). These results form a new digital image that may be displayed or recorded in pictorial format or may it be further manipulated by additional computer programs. Depending on the quality of a given degraded image, each of these improvement factors becomes an important subtopic separately, namely, de noising, contrast enhancement, white balance, de blurring, de mosaic king, de blocking, super-resolution, imprinting, sharpening, smoothing, interpolation, gamma correction, chromaticity enhancement, and so forth[1]
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