Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

Bu, Lijing; Dai, Dong; Zhang, Zhengpeng; Yang, Yin; Deng, Mingjun

doi:10.3390/rs15133225

Cited by 8 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inspired by residual dense networks [29], Zhao et al [30] designed a multilevel high-resolution network (HRNet) comprised of residual dense blocks and residual global blocks to remove image residuals and enhance the perceptual domain, respectively. To better enhance the spatial resolution of HSIs while preserving their spectral characteristics, a hybrid convolution and spectral symmetry preservation network was proposed in [31]. Zhang et al [32] designed a pixel-aware network with different receptive field sizes for SSR.…”

Section: Cnn-based Ssr Approachesmentioning

confidence: 99%

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Liao,

He,

Mao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs. In recent years, convolutional neural networks (CNNs) have become widely adopted in SSR research, primarily because of their exceptional ability to extract features. However, most current CNN-based algorithms are weak in terms of extracting the spectral features of HSIs. While certain algorithms can reconstruct HSIs through the fusion of spectral and spatial data, their practical effectiveness is hindered by their substantial computational complexity. In light of these challenges, we propose a lightweight network, Transformer with convolutional spectral self-attention (TCSSA), for SSR. TCSSA comprises a CNN-Transformer encoder and a CNN-Transformer decoder, in which the convolutional spectral self-attention blocks (CSSABs) are the basic modules. Multiple cascaded encoding and decoding modules within TCSSA facilitate the efficient extraction of spatial and spectral contextual information from HSIs. The convolutional spectral self-attention (CSSA) as the basic unit of CSSAB combines CNN with self-attention in the transformer, effectively extracting both spatial local features and global spectral features from HSIs. Experimental validation of TCSSA’s effectiveness is performed on three distinct datasets: GF5 for remote sensing images along with CAVE and NTIRE2022 for natural images. The experimental results demonstrate that the proposed method achieves a harmonious balance between reconstruction performance and computational complexity.

show abstract

Section: Cnn-based Ssr Approachesmentioning

confidence: 99%

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Liao,

He,

Mao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The self-attention (SA) mechanism has been of wide concern since it was put forward because it not only considers global aspects but also focuses on key points and can effectively establish the global dependence between features. At present, the SA mechanism has achieved good performance in image super-resolution reconstruction [34] and image generation [35]. Zhang et al [36] introduced the SA mechanism module into the GAN to learn the correlation between features to improve the quality of generated pictures.…”

Section: Introductionmentioning

confidence: 99%

Cross-View Gait Recognition Model Combining Multi-Scale Feature Residual Structure and Self-Attention Mechanism

Wang,

Guo,

2023

IEEE Access

View full text Add to dashboard Cite

In the cross-view condition, the gait recognition rate caused by the vastly different gait silhouette maps is substantially reduced. To improve the accuracy of gait recognition under cross-view conditions, this paper proposes a cross-view gait recognition network model combining multi-scale feature residual module (MFRM) and self-attention (SA) mechanism based on Generative Adversarial Network (GAN). First, the local and global feature information in the input gait energy image is fully extracted using the MFRM. Then, the SA mechanism module is used to adjust the information of channel dimensions and capture the association between feature information and is introduced into both the generator and discriminator. Next, the model is trained using a two-channel network training strategy to avoid the pattern collapse problem during training. Finally, the generator and discriminator are optimized to improve the quality of the generated gait images. This paper conducts experiments using the CASIA-B and OU-MVLP public datasets. The experiments demonstrate that the MFRM can better obtain the local and global feature information of the images. The SA mechanism module can effectively establish global dependencies between features, so that the generated gait images have clearer and richer detail information. The average Rank-1 recognition accuracies of the results in this paper reach 91.1% and 97.8% on the two datasets respectively, which are both better than the current commonly used algorithms, indicating that the network model in this paper can well improve the gait recognition accuracy across perspectives.INDEX TERMS cross-view, gait recognition, residual module, self-attention mechanism, two-channel network.

show abstract

“…Multi-temporal SAR provides a more comprehensive and detailed view of surface and dynamic changes [2]. SAR not only overcomes the limitations of time and weather conditions in ground observation [3], specifically demonstrating the capability of all-weather, all-time, and the continuous observation of moving targets [4,5], but also exhibits a certain ability to penetrate vegetation, soil, and occlusions [6,7]. Given these unique advantages of SAR, its applications are extremely diverse.…”

Section: Introductionmentioning

confidence: 99%

Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform

Bu,

Zhang,

Zhang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The presence of speckle noise severely hampers the interpretability of synthetic aperture radar (SAR) images. While research on despeckling single-temporal SAR images is well-established, there remains a significant gap in the study of despeckling multi-temporal SAR images. Addressing the limitations in the acquisition of the “superimage” and the generation of ratio images within the RABASAR despeckling framework, this paper proposes an enhanced framework. This enhanced framework proposes a direction-based segmentation approach for multi-temporal SAR non-local means filtering (DSMT-NLM) to obtain the “superimage”. The DSMT-NLM incorporates the concept of directional segmentation and extends the application of the non-local means (NLM) algorithm to multi-temporal images. Simultaneously, the enhanced framework employs a weighted averaging method based on wavelet transform (WAMWT) to generate superimposed images, thereby enhancing the generation process of ratio images. Experimental results demonstrate that compared to RABASAR, Frost, and NLM, the proposed method exhibits outstanding performance. It not only effectively removes speckle noise from multi-temporal SAR images and reduces the generation of false details, but also successfully achieves the fusion of multi-temporal information, aligning with experimental expectations.

show abstract

Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

Cited by 8 publications

References 48 publications

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Cross-View Gait Recognition Model Combining Multi-Scale Feature Residual Structure and Self-Attention Mechanism

Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform

Contact Info

Product

Resources

About