Super-resolution mapping via multi-dictionary based sparse representation

Huang, Huijuan; Yu, Jing; Sun, Weidong

doi:10.1109/icassp.2014.6854256

Cited by 38 publications

(14 citation statements)

References 14 publications

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“…Dictionary learning for sparse representation of signals is one of the active topics in various areas such as compression, denoising, inpainting, super-resolution, classification, and source separation [1][2][3][4][5]. Dictionary is a collection of atoms which can represent each training data by a linear combination of the atoms.…”

Section: Introductionmentioning

confidence: 99%

Generalized adaptive weighted recursive least squares dictionary learning

2016

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Section: Introductionmentioning

confidence: 99%

Generalized adaptive weighted recursive least squares dictionary learning

2016

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“…To address these drawbacks, some improvements have been made in subsequent works [14][15][16][17][18]. However, the premise is that the two images should be aligned, otherwise the performance will be significantly degraded [19][20][21]. Compared with the former, single HSI super-resolution methods require no auxiliary images, which are more convenient to apply to the real scenario.…”

Section: Introductionmentioning

confidence: 99%

Difference Curvature Multidimensional Network for Hyperspectral Image Super-Resolution

Zhang

et al. 2021

Remote Sensing

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In recent years, convolutional-neural-network-based methods have been introduced to the field of hyperspectral image super-resolution following their great success in the field of RGB image super-resolution. However, hyperspectral images appear different from RGB images in that they have high dimensionality, implying a redundancy in the high-dimensional space. Existing approaches struggle in learning the spectral correlation and spatial priors, leading to inferior performance. In this paper, we present a difference curvature multidimensional network for hyperspectral image super-resolution that exploits the spectral correlation to help improve the spatial resolution. Specifically, we introduce a multidimensional enhanced convolution (MEC) unit into the network to learn the spectral correlation through a self-attention mechanism. Meanwhile, it reduces the redundancy in the spectral dimension via a bottleneck projection to condense useful spectral features and reduce computations. To remove the unrelated information in high-dimensional space and extract the delicate texture features of a hyperspectral image, we design an additional difference curvature branch (DCB), which works as an edge indicator to fully preserve the texture information and eliminate the unwanted noise. Experiments on three publicly available datasets demonstrate that the proposed method can recover sharper images with minimal spectral distortion compared to state-of-the-art methods. PSNR/SAM is 0.3–0.5 dB/0.2–0.4 better than the second best methods.

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“…However, these methods cause HSI blur, which is not conducive to practical application. In recent years, some single-image HSI SR methods based on sparse representation were proposed [14,15]. In order to exploit the self-similarity in spatial and spectral domains, a multi-dictionary sparse representation method was proposed for HSI SR [15].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, some single-image HSI SR methods based on sparse representation were proposed [14,15]. In order to exploit the self-similarity in spatial and spectral domains, a multi-dictionary sparse representation method was proposed for HSI SR [15]. Recently, super-resolution based on deep learning for natural images develops rapidly [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution

Tang

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The spectral resolution of hyperspectral images (HSIs) is very high. Nevertheless, their spatial resolution is low due to various hardware limitations. Therefore, it is important to study HSI super-resolution to improve their spatial resolution. In this paper, for hyperspectral single-image super-resolution, we propose a multi-scale feature fusion and aggregation network with 3D convolution (MFFA-3D) by cascading the MFFA-3D block. The MFFA-3D block includes group multi-scale feature fusion part and multi-scale feature aggregation part. In group multi-scale feature fusion part, a novel group multi-scale feature fusion method is proposed. Group feature fusion module and two-step multi-scale module are proposed in multi-scale feature aggregation part. In order to prevent spectral distortion, a spectral gradient loss function is proposed and combined with the mean square error (MSE) loss function to form the final loss function. Since the proposed super-resolution network is full 3D convolutional network, our method can perform direct super-resolution transfer even if the number of the bands of test images is different from that of training images. The experiments over simulated and real hyperspectral images demonstrate the superiority of the proposed method in terms of qualitative and quantitative evaluation.

show abstract

Super-resolution mapping via multi-dictionary based sparse representation

Cited by 38 publications

References 14 publications

Generalized adaptive weighted recursive least squares dictionary learning

Generalized adaptive weighted recursive least squares dictionary learning

Difference Curvature Multidimensional Network for Hyperspectral Image Super-Resolution

Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution

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