Multistage reaction‐diffusion equation network for image super‐resolution
Abstract Deep learning‐based models have progressed considerably in single‐image super‐resolution. A high‐resolution pattern generation task is performed at the end of convolution neural networks (CNNs) with some convolution‐based operations in these models. However, this process may be difficult be...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-10-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12279 |
| Summary: | Abstract Deep learning‐based models have progressed considerably in single‐image super‐resolution. A high‐resolution pattern generation task is performed at the end of convolution neural networks (CNNs) with some convolution‐based operations in these models. However, this process may be difficult because all the work is done through the remarkable learning ability of CNN without any specific learning target. Reaction‐diffusion equation (RDE) is a mechanism involved in the pattern generation process that can serve as a guide for super‐resolution. It is proposed to embed RDE into a super‐resolution network by designing a reaction‐diffusion process block (RDPB) in this study. The proposed RDPB uses Euler method for iteratively solving one particular RDE, which is determined by the parameter generated through CNN. Accordingly, this module guides and leads the CNN in generating patterns for image super‐resolution. Moreover, a multistage framework is constructed to guide each network module further. On the basis of these two designs, the multistage reaction‐diffusion equation network is proposed for image super‐resolution. Experimental results demonstrated that the proposed model can obtain findings consistent with the conclusions of state‐of‐the‐art methods with a relatively shallow structure and small model size. |
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| ISSN: | 1751-9659 1751-9667 |