Wavelet detail perception network for single image super-resolution

Hsu, Wei‐Yen; Jian, Pei-Wen

doi:10.1016/j.patrec.2022.12.021

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And in recent years recursive learning has been combined with many other methods to achieve better performance. Such as RDRN [83] in the common recursive network architecture combined with an efficient attention mechanism to achieve good performance gain, WRSANe [84] recursively processes wavelet components at different frequencies for better visuals. RGT [85] designed recursive generalization self‐attention, Aggregation of input features facilitates efficient global information processing.…”

Section: Classification Taxonomy Of the Sisr Network Architecturementioning

confidence: 99%

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

Zhang,

Han,

Zhu

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Classification Taxonomy Of the Sisr Network Architecturementioning

confidence: 99%