Deep Convolutional Network Aided by Non-Local Method for Hyperspectral Image Denoising

This paper introduces a new hyperspectral image (HSI) denoising method called Non-local Convolutional Neural Network Denoiser (NL-CNND). The technique exploits data in four bands adjacent to the target one as additional information for the restoring process, and it uses a pre-denoising step based on...

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Bibliographic Details
Published inIEEE access Vol. 11; p. 1
Main Authors De Oliveira, Gabriel A., Almeida, Larissa M., De Lima, Eduardo R., Meloni, Luis Geraldo P.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial neural networks
Band Correlation
BM4D
Convolutional neural networks
Convolutive Neural Network
Denoising
Hyperspectral Images
Hyperspectral imaging
Image restoration
Neural networks
NLM
Noise level
Noise levels
Noise measurement
Noise reduction
Principal component analysis
Signal to noise ratio
Three-dimensional displays
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Summary:This paper introduces a new hyperspectral image (HSI) denoising method called Non-local Convolutional Neural Network Denoiser (NL-CNND). The technique exploits data in four bands adjacent to the target one as additional information for the restoring process, and it uses a pre-denoising step based on BM4D. All the bands paired with their pre-denoised versions in a second step feed a Convolutional Neural Network. To network generalization, one of the inputs is the noise level of the input image, allowing a single model to work with different noise levels. This restoration technique overcomes quality when compared to current eight classical and neural methods. The results show higher PSNR, SSIM, and SAM metrics than all the other restoration methods, surpassing those achieved using BM4D alone. Besides, the results show a higher level of detail visually while at the same time reducing over-smoothing on the input images' features. The paper also includes an algorithm for complete image restoration, allowing for denoising full-sized HSIs independent of their shape. The dataset creation used for network training is detailed, based on a small set of available hyperspectral images, encompassing data normalization, conversion, and storage.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3273486