Variational model for simultaneously image denoising and contrast enhancement

Optics Express
Wei WangMichael K Ng

Abstract

The performance of contrast enhancement is degraded when input images are noisy. In this paper, we propose and develop a variational model for simultaneously image denoising and contrast enhancement. The idea is to propose a variational approach containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform and the noise of the image can be removed. In the proposed model, a histogram equalization term is considered for image contrast enhancement, a total variational term is incorporate to remove the noise of the input image, and a fidelity term is added to keep the structure and the texture of the input image. The existence of the minimizer and the convergence of the proposed algorithm are studied and analyzed. Experimental results are presented to show the effectiveness of the proposed model compared with existing methods in terms of several measures: average local contrast, discrete entropy, structural similarity index, measure of enhancement, absolute measure of enhancement, and second derivative like measure of enhancement.

References

Sep 21, 2004·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhou WangEero P Simoncelli
Apr 5, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Marcelo BertalmíoAlessandro Rizzi
Feb 16, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·T F Chan, C K Wong
Jul 23, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Mila Nikolova, Gabriele Steidl
Oct 28, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Mila Nikolova, Gabriele Steidl

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Citations

Jun 19, 2021·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Rizwan KhanBing Li

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