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Image Super-resolution via Dynamic Network by Tian, Chunwei; Zhang, Xuanyu; Zhang, Qi; Yang, Mingming; Ju, Zhaojie is a scholarly article available to read on EtoBox.

What is Image Super-resolution via Dynamic Network about?

Convolutional neural networks (CNNs) depend on deep network architectures to extract accurate information for image super-resolution. However, obtained information of these CNNs cannot completely express predicted high-quality images for complex scenes. In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement 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 refinement block utilizes 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.

Author

Tian, Chunwei; Zhang, Xuanyu; Zhang, Qi; Yang, Mingming; Ju, Zhaojie

Published

2023

Language

EN