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

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 existi...

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Vydáno v:Multimedia tools and applications Ročník 81; číslo 2; s. 2529 - 2555
Hlavní autoři: Paul, Arati, Kundu, Ahana, Chaki, Nabendu, Dutta, Dibyendu, Jha, C. S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2022
Springer Nature B.V
Témata:
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Feature extraction
Hyperspectral imaging
Image enhancement
Machine learning
Multimedia Information Systems
Neural networks
Noise
Noise reduction
Performance evaluation
Remote sensing
Special Purpose and Application-Based Systems
Discrete wavelet transformation
Residual learning
Hyperspectral image
Convolutional neural networks
Denoising
ISSN:1380-7501, 1573-7721
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Shrnutí: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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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-11689-z