Hyperspectral Image Denoising With Dual Deep CNN

Shan, Wei; Liu, Peng; Mu, Lin; Cao, Caihong; He, Guojin

doi:10.1109/access.2019.2955810

Cited by 19 publications

(3 citation statements)

References 56 publications

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“…They trained the network in wavelet domain for the effective denoising. Other deep learning based denoising method for satellite imagery can be found in [22], [23], and [24].…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN

Song,

Jeong,

Park

et al. 2020

Preprint

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Multi-spectral satellite imaging sensors acquire various spectral band images such as red (R), green (G), blue (B), near-infrared (N), etc. Thanks to the unique spectroscopic property of each spectral band with respective to the objects on the ground, multi-spectral satellite imagery can be used for various geological survey applications. Unfortunately, image artifacts from imaging sensor noises often affect the quality of scenes and have negative impacts on the applications of satellite imagery. Recently, deep learning approaches have been extensively explored for the removal of noises in satellite imagery. Most deep learning denoising methods, however, follow a supervised learning scheme, which requires matched noisy image and clean image pairs that are difficult to collect in real situations. In this paper, we propose a novel unsupervised multispectral denoising method for satellite imagery using wavelet subband cycle-consistent adversarial network (WavCycleGAN). The proposed method is based on unsupervised learning scheme using adversarial loss and cycle-consistency loss to overcome the lack of paired data. Moreover, in contrast to the standard imagedomain cycleGAN, we introduce a wavelet subband domain learning scheme for effective denoising without sacrificing high frequency components such as edges and detail information. Experimental results for the removal of vertical stripe and wave noises in satellite imaging sensors demonstrate that the proposed method effectively removes noises and preserves important high frequency features of satellite images.

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“…They trained the network in wavelet domain for the effective denoising. Other deep learning based denoising method for satellite imagery can be found in [22], [23], and [24].…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN

Song,

Jeong,

Park

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Meanwhile, it was the first time that the CNN was used for image denoising. Since then, many kinds of convolution networks with very deep architecture have been developed [32][33][34].…”

Section: Traditional Denoising Algorithms and Early Cnn Structures Fo...mentioning

confidence: 99%

Multi-wavelet residual dense convolutional neural network for image denoising

Wang,

Yu,

2020

Preprint

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Networks with large receptive field (RF) have shown advanced fitting ability in recent years. In this work, we utilize the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN), one of the state-of-art networks with large RF, as the backbone, and insert residual dense blocks (RDBs) in its each layer. We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency. Meanwhile, this approach also provides a possibility of absorbing advantages of multiple architectures in a single network without conflicts. The performance of the proposed method has been demonstrated in extensive experiments with a comparison with existing techniques.

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“…The performance of these approaches may further be extended to blind image denoising using patch based noise estimation strategies entailed in [15], [16]. In addition, to these classical approaches, recent advancements in machine learning methodologies have embarked on image denoising problem [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Framework for Reduction of Mixed Noise via Low Rank Noise Estimation

et al. 2022

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In this paper, an innovative hybridized deep learning framework (EN-CNN) is presented for image noise reduction where the noise originates from heterogeneous sources. More specifically, EN-CNN is applied to the benchmark natural images affected by a mixture of additive white gaussian noise (AWGN) and impulsive noise (IN). Reduction of mixed noise (AWGN and IN) is relatively more involved as compared to removing simply one type of noise. In fact, mitigating the impact of a mixture of multiple noise types becomes exceedingly challenging due to simultaneous presence of different noise statistics. Although, various effective deep learning approaches such as [1] and the classical state-of-the-art approaches like WNNM [2] have been used to suppress AWGN noise only, the same techniques are not suitable in case of mixed noise. In this context, EN-CNN can not only infer changed noise statistics but can also effectively eliminate residual noise. Firstly, EN-CNN employs the classical method of neighborhood filtering followed by non-local low rank estimation to respectively reduce IN noise and estimate the residual noise characteristics after reducing IN noise. As a result of this step, we obtain a pre-processed image with residual noise statistics. Secondly, convolutional neural network (CNN) is applied to the pre-processed image based on the noise statistics inferred in the first step. This two pronged strategy, in conjunction with the deep learning mechanism, effectively handles the mixed noise suppression. As a result, the suggested framework yields promising results as compared to various state-of-the-art approaches.

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Hyperspectral Image Denoising With Dual Deep CNN

Cited by 19 publications

References 56 publications

Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN

Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN

Multi-wavelet residual dense convolutional neural network for image denoising

Hybrid Deep Learning Framework for Reduction of Mixed Noise via Low Rank Noise Estimation

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