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Multi-scale recursive codec network with authority parameters (MRCN-AP) for RFID multi-label deblurring

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Abstract

The dynamic non-uniform blur caused by Radio Frequency Identification (RFID) multi-label motion seriously affects the identification and location of labels. It is an ill-posed inverse problem for that the blur kernel and sharp image are unknown. The traditional method of removing the blur is very time-consuming. In this work, we propose Multi-scale Recursive Codec Network based on the Authority Parameter (MRCN-AP) to deblur RFID multi-label images in a vision-based RFID multi-label 3D measurement system. This network is composed of a stack of three encoder-decoder subnets of different scales, which restores the blurry image in an end-to-end manner, and extracts the detail edge on each scale effectively from coarse to fine. The proposed authority parameters reduce the parameters memory of redundant networks and improve the speed of the deblurring network. Also, we propose new large-scale RFID multi-label blur-sharp image pairs captured by the dual CCD camera. The proposed model is implemented on an extended dataset. We prove that our method improves the speed by at least 0.55 s, and also increases Peak Signal to Noise Ratio (PSNR) by 2.43dB. Besides, better visual effects are obtained by MRCN-AP deblurring network for RFID multi-label image, which is more conducive to subsequent positioning and optimization.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NNSFC) (61771240), Fund Project of Jiangsu Engineering Laboratory for Lake Environment Remote Sensing Technologies (JSLERS-2018-003), China Postdoctoral Science Foundation (2016T90452), and Six Talent Peaks Project in Jiangsu Province of China (XYDXX-058).

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Li, L., Yu, X., Liu, Z. et al. Multi-scale recursive codec network with authority parameters (MRCN-AP) for RFID multi-label deblurring. Multimed Tools Appl 80, 32149–32169 (2021). https://doi.org/10.1007/s11042-021-11216-0

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