Attention based deep convolutional U-Net with CSA optimization for hyperspectral image denoising

Murugesan, Ramya; Nachimuthu, Nandhagopal; Prakash, G.

doi:10.1016/j.infrared.2022.104531

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Masoud Moradi [19] determined the optimal wavelet functions for ocean color time series data. Xuya Liu et al [20] developed the WV-KWF algorithm, based on low-rank approximation, which improves the retention of image structures and denoising effects.Ramya Murugesan et al [21] proposed a deep convolutional U-Net model with attention mechanisms, further enhancing the performance of hyperspectral image denoising through optimized algorithm parameters. These studies have not only driven the development of remote sensing image denoising techniques but also provided robust technical support for applications in related domains.…”

Section: Introductionmentioning

confidence: 99%

Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Xiong,

Wang,

Qiao

et al. 2024

Preprint

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The computational efficiency is low when the vast volume of unmanned aerial vehicle airborne gamma-ray spectrum (UAVAGS) data is handled by wavelet denoising in CPU. So, a CUDA-based GPU parallel solution is recommended to resolve this issue in this paper. This proposed solution aims to significantly enhance the efficiency of parallel acceleration for wavelet denoising of UAVAGS data. In the preliminary stage, experiments were conducted with varying block sizes to investigate the influence of different block sizes on processing time. The objective was to identify the most suitable block size for efficiently processing UAVAGS data. Subsequently, a performance evaluation was conducted by comparing the acceleration ratios of GPU and CPU for different data volumes, as well as varying wavelet basis functions under the same data volume conditions. Finally, by intentionally introducing noise, calculations were performed to determine the optimal wavelet basis function concerning signal-to-noise ratio after denoising. The research findings indicate that the optimal two-dimensional block size falls within the range of 64×64 to 128×128. The majority of wavelet basis functions achieved acceleration ratios exceeding 100-fold in total processing time, with the coif5 wavelet basis function reaching an acceleration ratio of 185-fold. Comparative analysis of various denoising functions revealed that, under low signal-to-noise ratios, these functions exhibited insufficient denoising effects, while at high signal-to-noise ratios, there was a risk of excessive denoising. However, significant denoising effects were observed when employing hard thresholding with coif5, soft thresholding, and an improved thresholding method with db3.

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Section: Introductionmentioning

confidence: 99%

Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Xiong,

Wang,

Qiao

et al. 2024

Preprint

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show abstract

“…To date, numerous HSI denoising methods have been proposed, including filtering methods [11][12][13][14][15], the nonlocal self-similarity algorithm [16][17][18][19][20][21], total variation model-based approaches [22][23][24][25][26][27][28][29], low-rank property-based methods (LRs) [8,9,[30][31][32][33][34][35][36][37][38], and deep learningbased methods [10,[39][40][41][42][43]. Common filtering methods include the bilateral filter [11], blockmatching 3D filtering (BM3D) [12], BM4D [13], VBM4D [14], and PCA-BM3D [15].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, with the rapid development of artifact intelligence technology, deep learning-based methods have achieved excellent HSI denoising results due to the powerful feature representation capability of their deep architectures. Most deep learning-based HSI denoising methods are designed by convolution neural networks (CNNs) [10,[39][40][41][42][43] with multiscale and multilevel features [39]; these include the deep spatio-spectral Bayesian posterior based on a CNN [40], the HSI single-denoising CNN (SDeCNN) [41], the LR-Net [10], and the attention-based deep convolutional U-Net [42]. Although the use of deep learning methods can be seen as a breakthrough in the HSI denoising field, at least in part, they are still associated with a number of problems, including the difficulty of addressing the numerous high-quality HSI pairs (clean and noisy) required for training the model, as well as lengthy training times and poor generalization capabilities with respect to different types of noise.…”

Section: Introductionmentioning

confidence: 99%

Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising

Han,

Pan,

Ding

et al. 2023

Remote Sensing

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Hyperspectral image (HSIs) denoising is a preprocessing step that plays a crucial role in many applications used in Earth observation missions. Low-rank tensor representation can be utilized to restore mixed-noise HSIs, such as those affected by mixed Gaussian, impulse, stripe, and deadline noises. Although there is a considerable body of research on spatial and spectral prior knowledge concerning subspace, the correlation between the spectral continuity and the nonlocal sparsity of the spectral and spatial factors is not yet fully understood. To address this deficiency, in the present study, we determined the correlation between these factors using a cascaded technique, and we describe in this paper the double-factor tensor cascaded-rank (DFTCR) minimization method that was used. The information existing in the nonlocal sparsity property of the spatial factor was employed to promote a geometrical feature representation, and a tensor cascaded-rank minimization approach was introduced as a nonlocal self-similarity to promote restoration quality. The continuity between the difference and nonlocal gradient sparsity constraints of the spectral factor was also introduced to learn the basis. Furthermore, to estimate the solutions of the proposed model, we developed an algorithm based on the alternating direction method of multipliers (ADMM). The performance of the DFTCR method was tested by a comparison with eleven established denoising methods for HSIs. The results showed that the proposed DFTCR method exhibited superior performance in the removal of mixed noise from HSIs.

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Machine Learning for Image Denoising: A Review

Wu,

Wang,

Chen

et al. 2024

Lecture Notes in Electrical Engineering

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Attention based deep convolutional U-Net with CSA optimization for hyperspectral image denoising

Cited by 5 publications

References 26 publications

Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising

Machine Learning for Image Denoising: A Review

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