A Novel Attention Enhanced Dense Network for Image Super-Resolution
| Autor: | Yu-Bin Yang, Yang-Hao Zhou, Zhong-Han Niu, Jian-Cong Fan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Kernel (image processing)
business.industry Computer science 0202 electrical engineering electronic engineering information engineering Quantitative Evaluations 020207 software engineering 020201 artificial intelligence & image processing Pattern recognition 02 engineering and technology Artificial intelligence business Convolutional neural network Superresolution |
| Zdroj: | MultiMedia Modeling ISBN: 9783030377304 MMM (1) |
| Popis: | Deep convolutional neural networks (CNNs) have recently achieved impressive performance in image super-resolution (SR). However, they usually treat the spatial features and channel-wise features indiscriminatingly and fail to take full advantage of hierarchical features, restricting adaptive ability. To address these issues, we propose a novel attention enhanced dense network (AEDN) to adaptively recalibrate each kernel and feature for different inputs, by integrating both spatial attention (SA) and channel attention (CA) modules in the proposed network. In experiments, we explore the effect of attention mechanism and present quantitative and qualitative evaluations, where the results show that the proposed AEDN outperforms state-of-the-art methods by effectively suppressing the artifacts and faithfully recovering more high-frequency image details. |
| Databáze: | OpenAIRE |
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