Adaptive power-law and cdf based geometric transformation for low contrast image enhancement
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Adaptive power-law and cdf based geometric transformation for low contrast image enhancement Kanishka Sarkar1
· Tanmoy Kanti Halder2 · Ardhendu Mandal3
Received: 23 June 2020 / Revised: 15 September 2020 / Accepted: 29 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Image enhancement is a technique that manipulates an image to make it more meaningful and effective to user specific problem. In most of the enhancement techniques, input image intensities are transformed into either higher order or lower order intensities according to the designed algorithmic characteristic. But, in certain cases the input intensities might require to be transformed in a balanced combination of both higher and lower order intensity. Moreover, 2D Geometric Transformation is mainly used to transform the objects presents in an image. Here a contemplative fusion of gamma and 2D Geometric Transformation concept has been used for intensity transformation. The proposed method first divides the histogram into three sub-sections according to the homogeneity value representing the dark, gray and bright section of histogram. Then each sub-section is transformed locally using adaptive gamma and 2D Geometric scaling transformation. These transformed sub-sections are merged again by employing 2D translation operation. On the other hand, a global gamma transformation is obtained for entire histogram. At last, the final transformation matrix is obtained by combining previously computed local and global transformation. The comparison of this technique with other state of art technique has been discussed to depict the significance of the proposed method. The proposed method gives a new and innovative dimension of image enhancement. Keywords Geometric transformation based enhancement · Image enhancement · Low contrast image · Power-law transformation Kanishka Sarkar
[email protected] Tanmoy Kanti Halder [email protected] Ardhendu Mandal [email protected] 1
Ananda Chandra College, Jalpaiguri, West Bengal, 735101, India
2
Prasannadeb Women’s College, Jalpaiguri, West Bengal, 735101, India
3
University of North Bengal, Darjeeling, West Bengal, 734014, India
Multimedia Tools and Applications
1 Introduction Image pre-processing is very important and fundamental part of any type of digital image processing because it reduces computational complexity for the further processing. It is mainly used to optimize the image by suppressing the insignificant data and highlighting the important features of the image. Pre-processing of an image can be classified in different categories [25], but one of the important pillars among them is Image enhancement. Image enhancement includes intensity transformation, noise filtering, interpolation and magnification, edge enhancement and similar type of operation [6]. An effective and prominent result can be obtained by setting the enhancing parameters properly, but it is quite difficult to determine the exact value of the parameters [12]. Here, a dynamic met
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