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.

射频识别（RFID）多标签运动引起的动态非均匀模糊严重影响标签的识别和定位。由于模糊核和清晰图像未知，这是一个不适定的逆问题。传统的去除模糊的方法非常耗时。在这项工作中，我们提出了基于权威参数 (MRCN-AP) 的多尺度递归编解码网络，以在基于视觉的 RFID 多标签 3D 测量系统中对 RFID 多标签图像进行去模糊。该网络由三个不同尺度的编码器-解码器子网堆叠组成，以端到端的方式还原模糊图像，并从粗到细有效地提取每个尺度上的细节边缘。提出的权限参数减少了冗余网络的参数记忆，提高了去模糊网络的速度。此外，我们提出了由双 CCD 相机捕获的新型大规模 RFID 多标签模糊清晰图像对。所提出的模型是在扩展数据集上实现的。我们证明我们的方法将速度提高了至少 0.55 秒，并且还将峰值信噪比 (PSNR) 提高了 2.43d乙。此外，MRCN-AP去模糊网络对RFID多标签图像获得了更好的视觉效果，更有利于后续的定位和优化。