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

• Published in: Multimedia tools and applications Vol. 80; no. 21-23; pp. 32149 - 32169
• Main Authors: Li, Lin, Yu, Xiaolei, Liu, Zhenlu, Zhao, Zhimin, Zhang, Ke, Zhou, Shanhao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: CCD cameras; Codec; Coders; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Encoders-Decoders; Inverse problems; Multimedia Information Systems; Optimization; Parameters; Radio frequency identification; Signal to noise ratio; Special Purpose and Application-Based Systems; Visual effects; Visual signals; Computer vision; Blind deblurring; Recurrent neural network; Convolution neural network; RFID tags
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11216-0

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 2.43 d B . 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.