research-article

Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising

Authors:

Dibyendu Dutta,

C. S. JhaAuthors Info & Claims

Multimedia Tools and Applications, Volume 81, Issue 2

Pages 2529 - 2555

https://doi.org/10.1007/s11042-021-11689-z

Published: 01 January 2022 Publication History

Abstract

Denoising of hyperspectral images (HSIs) is an important preprocessing step to enhance the performance of its analysis and interpretation. In reality, a remotely sensed HSI experiences disturbance from different sources and therefore gets affected by multiple noise types. However, most of the existing denoising methods concentrates in removal of a single noise type ignoring their mixed effect. Therefore, a method developed for a particular noise type doesn’t perform satisfactorily for other noise types. To address this limitation, a denoising method is proposed here, that effectively removes multiple frequently encountered noise patterns from HSI including their combinations. The proposed dual branch deep neural network based architecture works on wavelet transformed bands. The first branch of the network uses deep convolutional skip connected layers with residual learning for extracting local and global noise features. The second branch includes layered autoencoder together with subpixel upsampling that performs repeated convolution in each layer to extract prominent noise features from the image. Two hyperspectral datasets are used in the experiment to evaluate the performance of the proposed method for denoising of Gaussian, stripe and mixed noises. Experimental results demonstrate the superior performance of the proposed network compared to other state-of-the-art denoising methods with PSNR 36.74, SSIM 0.97 and overall accuracy 94.03 %.

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Cited By

Song C(2024)The Role of Transformer-cGANs Fusion in Digital MarketingJournal of Organizational and End User Computing10.4018/JOEUC.34791436:1(1-22)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/JOEUC.347914
Bajpai SSharma DAlam MChandel VPandey ATripathi S(2023)Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image SensorsJournal of Electrical and Computer Engineering10.1155/2023/89612712023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8961271

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 81, Issue 2

Jan 2022

1497 pages

ISSN:1380-7501

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2022

Accepted: 18 October 2021

Revision received: 02 August 2021

Received: 13 April 2021

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Cited By

Song C(2024)The Role of Transformer-cGANs Fusion in Digital MarketingJournal of Organizational and End User Computing10.4018/JOEUC.34791436:1(1-22)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/JOEUC.347914
Bajpai SSharma DAlam MChandel VPandey ATripathi S(2023)Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image SensorsJournal of Electrical and Computer Engineering10.1155/2023/89612712023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8961271

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