Wavelet Pyramid Recurrent Structure-Preserving Attention Network for Single Image Super-Resolution

Hsu, Wei‐Yen; Jian, Pei-Wen

doi:10.1109/tnnls.2023.3289958

Cited by 4 publications

(1 citation statement)

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“…Jindal et al (2021) [12] combined principal component analysis with DWT and Stationary WT for image fusion, the method has higher objective evaluation indicators and better visualization than other methods. Hsu and Jian (2023) [13] combined neural network with DWT for multi-focus image fusion. The proposed approach is superior to existing technologies in terms of performance and visualization.…”

Section: Introductionmentioning

confidence: 99%

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

Wang,

Xie,

Yang

2023

Preprint

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Greyscale image fusion plays a crucial role in computer vision and image processing. This paper proposes a novel algorithm for greyscale image fusion that enhances clarity and information retention of the resulting image. Firstly, the source image is decomposed using discrete fractional wavelet transform (DFRWT) to acquire the low and high frequency components. Secondly, considering the different characteristics of low and high frequencies, the spatial frequency technique is chosen to address the coefficients in the low frequency domain. In the high-frequency part, an improved chaotic genetic algorithm (ICGA) is used to optimize the high-frequency fusion coefficients, which can adaptively adjust the weighting to avoid the blurring and distortion problems that are likely to occur when the DFRWT processes the details, as a consequence, the features of the original image are better retained, making the fused image more natural and the details clearer. Finally, the inverse DFRWT is performed to get the fusion results. Simulation experiments prove the effectiveness of the new algorithm.

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Section: Introductionmentioning

confidence: 99%

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

Wang,

Xie,

Yang

2023

Preprint

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Network architecture for single image super‐resolution: A comprehensive review and comparison

Zhang,

Han,

Zhu

et al. 2024

IET Image Processing

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Single image super‐resolution (SISR) is a promising research direction in computer vision and image processing for improving the visual perception of low‐quality images. In recent years, deep learning algorithms have driven tremendous development in SR, and SR methods based on various network architectures have significantly improved the quality of reconstructed images. Although there has been a large amount of reviews focusing on SISR, few studies have focused specifically on network architectures for SISR. This paper aims to provide a systematic overview of the design ideas of SISR using multiple architectures, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Transformer, and Diffusion model. In addition, an experimental analysis and comparison of state‐of‐the‐art SR algorithms have been performed on publicly available quantitative and qualitative datasets. Finally, some future directions are discussed that may help other community researchers.

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Pan-Sharpening With Wavelet-Enhanced High-Frequency Information

Zhang,

He,

Yan

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Wavelet Pyramid Recurrent Structure-Preserving Attention Network for Single Image Super-Resolution

Cited by 4 publications

References 0 publications

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

A novel method of greyscale image fusion based on discrete fractional wavelet transform combined with improved chaotic genetic algorithm

Network architecture for single image super‐resolution: A comprehensive review and comparison

Pan-Sharpening With Wavelet-Enhanced High-Frequency Information

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