Lqaid: Localized Quality Aware Image Denoising Using Deep Convolutional Neural Networks

Reddy Dendi, Sathya Veera and Dev, Chander and Kothari, Narayan and Channappayya, Sumohana S. (2020) Lqaid: Localized Quality Aware Image Denoising Using Deep Convolutional Neural Networks. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, 4- 8 May 2020, Barcelona.

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Abstract

In this paper we propose the Localized Quality Aware Image Denoising (LQAID) technique for image denoising using deep convolutional neural networks (CNNs). LQAID relies on local quality estimates over global cues like noise standard deviation since the perceptual quality of a noisy image is typically spatially varying. Specifically, we use localized quality maps generated using DistNet, a spatial quality map estimation method. These quality maps are used to augment the noisy image and guide the denoising process. The augmented noisy image is denoised using a deep fully convolutional network (FCN) trained using mean square error (MSE) as the loss function. The proposed approach shows state-of-the-art performance both qualitatively and quantitatively on two vision datasets: TID 2008 and BSD500. We also show that the proposed approach possesses excellent generalization ability. Lastly, the proposed approach is completely blind since it neither requires information about the strength of the additive noise nor does it try to explicitly estimate it. © 2020 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Channappayya, Sumohana S.https://orcid.org/0000-0002-5687-0887
Item Type: Conference or Workshop Item (Paper)
Additional Information: We thank Visvesvaraya PhD scheme, Media Asia Lab, MeitY, Government of India for the financial support.
Uncontrolled Keywords: Acoustic noise; Additive noise; Audio signal processing; Convolution; Convolutional neural networks; Deep neural networks; Mean square error; Speech communication
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Nov 2022 09:34
Last Modified: 23 Nov 2022 09:34
URI: http://raiith.iith.ac.in/id/eprint/11227
Publisher URL: https://doi.org/10.1109/ICASSP40776.2020.9053056
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