Open Access

ARTICLE

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

Balakrishnan Natarajan^1,*, Pushpalatha Krishnan²

1 Department of Master of Computer Applications, Sona College of Technology, Salem – 636005, India
2 Department of Computer Applications, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India

* Corresponding Author: Balakrishnan Natarajan. Email:

Computer Systems Science and Engineering 2022, 42(2), 423-438. https://doi.org/10.32604/csse.2022.022376

Received 05 August 2021; Accepted 07 September 2021; Issue published 04 January 2022

Abstract

In existing methods for segmented images, either edge point extraction or preservation of edges, compromising contrast images is so sensitive to noise. The Degeneration Threshold Image Detection (DTID) framework has been proposed to improve the contrast of edge filtered images. Initially, DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images. This filter decomposes input images into base layers in the DTID framework. With minimal filtering time, Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation. In the DTID framework, Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise. This Shift-Invariant Filtering estimates value across edges for removing outliers (i.e., noise preserving base layers of the contrast image). The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter (SIDF). At last, Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image. It normalizes the translation and rotation of images. With this, Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy, rate, and filtering time on contrast images. Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54% and improves average contrast enhancement quality by 27% compared to GUMA, HMRF, SWT, and EHS. It provides better performance on the enhancement of average contrast enhancement quality by 28%, detection accuracy rate by 26%, and reduction in filtering time taken on contrast images by 30% compared to state-of-art methods.

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Keywords

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