A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition - Zhang, Yuejun; Wu, Zhixin; Liu, Shuzhi; Guo, Zhecheng; Chen, Qilai; Gao, Pingqi; Wang, Pengjun; Liu, Gang (Frontiers in Neuroscience, 2021)
The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.