Biomimetic adaptive memristive cellular neural network for image enhancement

Abstract

Cellular neural networks (CNN) have a simple local interconnect structure and high-speed parallel processing capability. As the basic model for a constructing artificial retina, CNNs can be applied to image enhancement in the machine vision field. However, existing image enhancement methods based on CNNs face several challenges. For example, when using common fixed templates, it is difficult to obtain ideal results when handling complex images in real-world applications. In addition, the lack of bionic considerations can cause failures when simulating the powerful global and local adaptive adjustment characteristics of human vision. Therefore, this paper proposes a biomimetic adaptive memristive CNN (BAM-CNN) that combines CNNs, human visual adaptive tri-Gaussian theory and memristor, and nano information devices. The proposed CNN can be used for image enhancement. Specifically, the memristive CNN is constructed based on emerging memristors that are programmable, non-volatile, and synapse-plastic. Merged with the tri-Gaussian model for the receptive field of neurons, an adaptive CNN template design algorithm for biomimetic image enhancement is proposed using the image processing features of the Gaussian kernel function and CNNs. In this paper, gray-scale and color images are taken as target examples in image enhancement experiments. The experimental results demonstrate that the proposed BAM-CNN significantly improves the global brightness, local contrast, and sharpness of the image. This paper provides a novel design and implementation scheme for adaptive templates of CNNs, which can improve CNNs biomimetic characteristics and hardware implementation feasibility. The proposed BAM-CNN can be used to develop innovative techniques for intelligent image processing besides image enhancement.