Memristive Crossbar Array-Based Computing Framework via DWT for Biomedical Image Enhancement

Here, we report the fabrication of Y2O3-based memristive crossbar array (MCA) by utilizing dual ion beam sputtering system, which shows high cyclic stability in the resistive switching behavior. Further, the obtained experimental results are validated with an analytical MCA based model, which exhibits extremely well fitting with the corresponding experimental data. Moreover, the experimentally validated analytical model is further used for biomedical image analysis, specifically computed tomography (CT) scan and magnetic resonance imaging (MRI) images by utilizing the 2-dimensional image decomposition technique. The different levels of decomposition are used for different threshold values which help to analyze the quality of the reconstructed image in terms of peak signal-to-noise ratio, structural similarity index and mean square error. For the MRI and CT scan images, at the first decomposition level, the data compression ratio of 21.01%, and 47.81% with Haar and 18.82%, and 46.05% with biorthogonal wavelet are obtained. Furthermore, the impact of brightness is also analyzed which shows a sufficient increment in the quality of output image by 103.72% and 18.59% for CT scan and MRI image, respectively for Haar wavelet. The proposed MCA based model for image processing is a novel approach to reduce the computation time and storage for biomedical engineering.