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A Model Learning Approach for Low Light Image Restoration

Malik, S and Soundararajan, R (2020) A Model Learning Approach for Low Light Image Restoration. In: Proceedings - International Conference on Image Processing, ICIP, 25-28 September 2020, Abu Dhabi; United Arab Emirates, Virtual, pp. 1033-1037.

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Official URL: https://dx.doi.org/10.1109/ICIP40778.2020.9191242


We study the problem of low light image restoration through contrast enhancement and denoising. We approach this problem by learning a model that relates a noisy low light and well lit image pair. The low light image is modeled to suffer from contrast distortion and additive noise. In particular, we model the loss of contrast through a global parametric function, which enables the estimation of the underlying noise. We then use a pair of convolutional neural network (CNN) models to learn the noise and the parameters of a function to achieve contrast enhancement. This contrast enhancement function is modeled as a linear combination of multiple gamma enhancers. We show through extensive evaluations that our Low Light Image Model for Enhancement Network (LLIMENet) achieves superior restoration performance when compared to other methods on several publicly available datasets. © 2020 IEEE.

Item Type: Conference Paper
Publication: Proceedings - International Conference on Image Processing, ICIP
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference Date: 25 September 2020 Through 28 September 2020; Conference Code:165772
Keywords: Additive noise; Convolutional neural networks; Image enhancement; Learning systems; Restoration, Contrast Enhancement; De-noising; Image pairs; Linear combinations; Low-light images; Model learning; Parametric functions; Show through, Image reconstruction
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 20 Jan 2021 06:18
Last Modified: 20 Jan 2021 06:18
URI: http://eprints.iisc.ac.in/id/eprint/67733

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