Image Denoising and Contrast Via Intensity Histogram Equalization Method


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Enhancement of the overall image contrast and sharpness of the image are the associated tasks to be performed in digital image processing. However, with the introduction of contrast enhancement it does not essentially leads to sharpness enhancement. The sharpness of an image, minute details has to be enhanced at the cost of enhancing noise as well. Existing variation models based upon Higher Order Derivatives (Variation-HOD) minimizes the curvature of all level lines in the image. With the application of an efficient minimization algorithm based upon the graph cuts model it failed to have the exact relationship on gradient flow. The curvature based models do not fit directly to exploit the max flow computation into the framework. Also Nonlinear Dynamic Range Adjustment (NDRA) method combined and updated the existing processing blocks but a comprehensive and efficient analysis of different chosen design parameters were not performed. The effective optimization technique did not implement the pre-processing steps to reduce the number of instructions, noise and improve the quality. To discover a work plan for conquering the noise defects, Intensity Histogram Equalization (IHE) method is proposed. IHE preprocess the image to remove the noise present in the image and enhance the image contrast for disparity enhancement and in that way introduces intensity to improve the brightness. The preprocessing in IHE method includes mask production, enlightenment equalization, and color normalization for efficient analysis of different chosen design parameters. Mask production labels the pixels, and Region-of-Interest (ROI) in the entire image excludes the background of the image to generate a binary image for each band. The histogram threshold rate was calculated using pixel value statistics for exact relationship maintenance on gradient flow. The experimental performance of IHE method is precise in terms of noise removal ratio, brightness quality efficiency, Max-flow computational intricacy, and false positive error. The noise removal ratio is approximately 11 % improved when compared with the Variation-HOD.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Histogram Equalization, Threshold Rate, Max-Flow Computation, Mask Production, Region-of-Interest, Optimization Technique, Gradient Flow, Preprocessing Steps

Full Text:

PDF


Refbacks




Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize