A low light natural image statistical model for joint contrast enhancement and denoising

Malik, S and Soundararajan, R (2021) A low light natural image statistical model for joint contrast enhancement and denoising. In: Signal Processing: Image Communication, 99 .

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Abstract

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods. © 2021

Item Type:	Journal Article
Publication:	Signal Processing: Image Communication
Publisher:	Elsevier B.V.
Additional Information:	The copyright for this article belongs to Elsevier B.V.
Keywords:	Gaussian noise (electronic); Mean square error, Contrast Enhancement; Gaussian scale mixture models; Indian institute of science; Minimum mean squared error; Multiple data sets; Posterior distributions; Statistical approach; Statistical modeling, Image enhancement
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering > Electrical Communication Engineering - Technical Reports
Date Deposited:	02 Dec 2021 11:53
Last Modified:	02 Dec 2021 11:53
URI:	http://eprints.iisc.ac.in/id/eprint/70059

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