A Wavelet-Based Asymmetric Convolution Network for Single Image Super-Resolution
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution(SISR) and obtained remarkable performance. However, most of the existing CNN-based SISR methods tend to produce over-smoothed outputs and miss some textural details. To address these issues...
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| Autores principales: | , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ffc04ca5aa0842d5bbeb8a176cd89c84 |
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