SQAD: Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising

Pan, Erting; Ma, Yong; Mei, Xiaoguang; Fan, Fan; Huang, Jun; Ma, Jiayi

doi:10.1109/tgrs.2022.3156646

Cited by 24 publications

(11 citation statements)

References 45 publications

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“…Recently, HSI-specific networks have also been developed to take advantage of both the spectral and spatial properties of HSI [11,10,37,38,39,40,41,42,43]. In particular, SS-CAN [11] is an HSI-specific denoising network, that combines group convolutions and attention modules to effectively exploit spatial and spectral information in images.…”

Section: Deep Learning Approaches To Hsi Denoisingmentioning

confidence: 99%

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Hyperspectral image denoising via self-modulating convolutional neural networks

Torun,

Yuksel,

Erdem

et al. 2024

Signal Processing

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Section: Deep Learning Approaches To Hsi Denoisingmentioning

confidence: 99%

“…More recently, attention-based methods were proposed to capture non-local features [39,40,41,42]. Yuan et.…”

Section: Deep Learning Approaches To Hsi Denoisingmentioning

confidence: 99%

Hyperspectral image denoising via self-modulating convolutional neural networks

Torun,

Yuksel,

Erdem

et al. 2024

Signal Processing

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“…In recent years, deep neural network (DNN) approaches have been popularly developed to directly learn a nonlinear mapping function from the space of noisy images to that of clean images [51]. With the employment of its strong learning capacity on large training samples, many DNN-based denoising methods have been proposed [18][19][20][21][52][53][54][55][56][57][58][59][60][61][62][63]. For example, Chen et al [52] used a loss-based scheme to learn filters from training data and formed a trainable nonlinear reaction diffusion (TNRD) model.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Zhang et al [56] used a memory-efficient hierarchical neural architecture to search for pleasing solutions. COLA-Net [57] and SQAD [58] introduced an attention mechanism into the DNN for denoising performance improvement. The residual network was further extended by Zhang et al [59] to a residual dense network.…”

Section: Deep Neural Networkmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.

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“…However, these model-driven HSI denoising methods concentrate on denoising only and ignore promoting HSI contrast and visibility. Most recently, the success of deep learning also stimulates the development of HSI denoising [24], [25], [26], and [27].…”

Section: Introductionmentioning

confidence: 99%

Low-Light Hyperspectral Image Enhancement

2022

IEEE Trans. Geosci. Remote Sensing

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Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (HSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of HSI restoration methods have been developed, yet their effectiveness in enhancing low-light HSIs is constrained. This work focuses on the lowlight HSI enhancement task, which aims to reveal the spatialspectral information hidden in darkened areas. To facilitate the development of low-light HSI processing, we collect a lowlight HSI (LHSI) dataset of both indoor and outdoor scenes. Based on Laplacian pyramid decomposition and reconstruction, we developed an end-to-end data-driven low-light HSI enhancement (HSIE) approach trained on the LHSI dataset. With the observation that illumination is related to the low-frequency component of HSI, while textural details are closely correlated to the high-frequency component, the proposed HSIE is designed to have two branches. The illumination enhancement branch is adopted to enlighten the low-frequency component with reduced resolution. The high-frequency refinement branch is utilized for refining the high-frequency component via a predicted mask. In addition, to improve information flow and boost performance, we introduce an effective channel attention block (CAB) with residual dense connection, which served as the basic block of the illumination enhancement branch. The effectiveness and efficiency of HSIE both in quantitative assessment measures and visual effects are demonstrated by experimental results on the LHSI dataset. According to the classification performance on the remote sensing Indian Pines dataset, downstream tasks benefit from the enhanced HSI. Datasets and codes are available: https://github.com/guanguanboy/HSIE.

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SQAD: Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising

Cited by 24 publications

References 45 publications

Hyperspectral image denoising via self-modulating convolutional neural networks

Hyperspectral image denoising via self-modulating convolutional neural networks

MD3: Model-Driven Deep Remotely Sensed Image Denoising

Low-Light Hyperspectral Image Enhancement

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