Joint Super-Resolution and Classification Based on Bidirectional Mapping and Multiple Constraints
Zijian Yuan, Kan Chang, Zhiquan Liu, Xinjie Wei, Boning Chen · 2023
Since discriminant features are insufficient in the low-resolution (LR) images, it is challenging to accurately classify them. To address this issue, this paper proposes a joint super-resolution (SR) and classification network (JSRCN) based on bidirectional mapping and multiple constraints. In JSRCN, there is a SR sub-network containing a forward mapping path and several backward mapping paths. In the forward mapping path, high resolution (HR) features are progressively reconstructed. On the other hand, the backward mapping paths are used to alleviate the hardship of directly learning a nonlinear mapping from the LR space to the HR space. To effectively restore discriminant features for the image classification sub-network, multiple perceptual loss and multi-scale feature loss are presented to enhance the representation ability for the multi-scale features in images with different resolutions. Experiments show that compared with other competing methods, the proposed method achieves the highest accuracy.