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Image super‐resolution via dynamic network 被引量:1

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摘要 Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.
出处 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第4期837-849,共13页 智能技术学报(英文)
基金 the TCL Science and Technology Innovation Fund the Youth Science and Technology Talent Promotion Project of Jiangsu Association for Science and Technology,Grant/Award Number:JSTJ‐2023‐017 Shenzhen Municipal Science and Technology Innovation Council,Grant/Award Number:JSGG20220831105002004 National Natural Science Foundation of China,Grant/Award Number:62201468 Postdoctoral Research Foundation of China,Grant/Award Number:2022M722599 the Fundamental Research Funds for the Central Universities,Grant/Award Number:D5000210966 the Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2021A1515110079。
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