An attention mechanism based convolutional network for satellite precipitation downscaling over China

Jing, Yinghong; Lin, Liupeng; Li, Xinghua; Li, Tongwen; Shen, Huanfeng

doi:10.48550/arxiv.2203.14812

Cited by 1 publication

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“…Anwar et al [59] combined densely concatenated residual blocks and Laplacian attention to learn the inter-and intralevel dependencies for accurate SR. Liu et al [60] described an aggregation framework that groups several residual modules for more efficient feature extraction. Jing et al [61] introduced an attention mechanism-based CNN comprising a global cross-attention and residual convolutional modules that consider the potential relationships between complicated characteristics.…”

Section: Residual Learningmentioning

confidence: 99%

Efficient face image super‐resolution with convenient alternating projection network

Chen

et al. 2023

IET Signal Processing

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The existing deep learning‐based face super‐resolution techniques can achieve satisfactory performance. However, these methods often incur large computational costs, and deeper networks generate redundant features. Some lightweight reconstruction networks also present limited representation ability because they ignore the entire contour and fine texture of the face for the sake of efficiency. Here, the authors propose a convenient alternating projection network (CAPN) for efficient face super‐resolution. First, the authors design a novel alternating projection block cascaded convolutional neural network to alternately achieve content consistency and learn detailed facial feature differences between super‐resolution and ground‐truth face images. Second, the self‐correction mechanism enabled the convolutional layer to capture faithful features that facilitate adaptive reconstruction. Moreover, a convenient connection operation can reduce the generation of redundant facial features while maintaining accurate reconstruction information. Extensive experiments demonstrated that the proposed CAPN can effectively reduce the computational cost while achieving competitive qualitative and quantitative results compared to state‐of‐the‐art super‐resolution methods.

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Section: Residual Learningmentioning

confidence: 99%

Efficient face image super‐resolution with convenient alternating projection network

Chen

et al. 2023

IET Signal Processing

View full text Add to dashboard Cite

show abstract

An attention mechanism based convolutional network for satellite precipitation downscaling over China

Cited by 1 publication

References 0 publications

Efficient face image super‐resolution with convenient alternating projection network

Efficient face image super‐resolution with convenient alternating projection network

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