Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

Hyperspectral imaging (HSI) is used in a wide range of applications such as remote sensing, space imagery, mineral detection, and exploration. Unfortunately, it is difficult to acquire hyperspectral images with high spatial and spectral resolution due to instrument limitations. The super-resolution techniques are used to reconstruct low-resolution hyperspectral images. However, traditional superresolution (SR) approaches do not allow direct use of both spatial and spectral information, which is a decisive for an optimal reconstruction. This paper proposes a single image SR algorithm for HSI. The algorithm uses the fact that the spatial and spectral information can be integrated to make an accurate estimate of the high-resolution HSI. To achieve this, two types of spatio- pectral downsampling, and a three-dimensional interpolation are proposed in order to increase coherence between the spatial and spectral information. The resulting reconstructions using the proposed method are up to 2 dB better than traditional SR approaches.

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Attention Mechanisms in CNN-Based Single Image Super-Resolution: A Brief Review and a New Perspective

Electronics ◽

10.3390/electronics10101187 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1187

Author(s):

Hongyu Zhu ◽

Chao Xie ◽

Yeqi Fei ◽

Huanjie Tao

Keyword(s):

Neural Network ◽

Super Resolution ◽

Single Image ◽

Research Papers ◽

Wide Range ◽

Non Local ◽

Image Super Resolution ◽

Basic Concepts ◽

New Perspective ◽

Single Image Super Resolution

With the advance of deep learning, the performance of single image super-resolution (SR) has been notably improved by convolution neural network (CNN)-based methods. However, the increasing depth of CNNs makes them more difficult to train, which hinders the SR networks from achieving greater success. To overcome this, a wide range of related mechanisms has been introduced into the SR networks recently, with the aim of helping them converge more quickly and perform better. This has resulted in many research papers that incorporated a variety of attention mechanisms into the above SR baseline from different perspectives. Thus, this survey focuses on this topic and provides a review of these recently published works by grouping them into three major categories: channel attention, spatial attention, and non-local attention. For each of the groups in the taxonomy, the basic concepts are first explained, and then we delve deep into the detailed insights and contributions. Finally, we conclude this review by highlighting the bottlenecks of the current SR attention mechanisms, and propose a new perspective that can be viewed as a potential way to make a breakthrough.

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SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution using Spatial and Spectral Information

IEEE Transactions on Computational Imaging ◽

10.1109/tci.2021.3070522 ◽

2021 ◽

pp. 1-1

Author(s):

Alireza Esmaeilzehi ◽

M. Omair Ahmad ◽

M.N.S. Swamy

Keyword(s):

Super Resolution ◽

Spectral Information ◽

Light Weight ◽

Single Image ◽

Image Super Resolution ◽

Single Image Super Resolution

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Deep fractal residual network for fast and accurate single image super resolution

Neurocomputing ◽

10.1016/j.neucom.2019.09.093 ◽

2020 ◽

Vol 398 ◽

pp. 389-398 ◽

Cited By ~ 1

Author(s):

Yanghao Zhou ◽

Jianfeng Dong ◽

Yubin Yang

Keyword(s):

Super Resolution ◽

Single Image ◽

Residual Network ◽

Image Super Resolution ◽

Single Image Super Resolution

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Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115890 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5890

Author(s):

Qiang Yu ◽

Feiqiang Liu ◽

Long Xiao ◽

Zitao Liu ◽

Xiaomin Yang

Keyword(s):

Deep Learning ◽

Real Time ◽

Super Resolution ◽

Model Complexity ◽

Practical Application ◽

Single Image ◽

Feature Maps ◽

Benchmark Datasets ◽

Image Super Resolution ◽

Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

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