Integrating Memristor-Based Median Filtering at the Sensor Front-End for Biomedical Image Enhancement

Abstract

Camera sensors often struggle to capture images in low-light conditions, leading to reduced brightness, contrast, and color fidelity, and increased noise that degrades the performance. Many methods have emerged for image enhancement but they often require slow processing and blur image, making them imperfect for real-world scenarios. This paper presents the first-ever Y<inf>2</inf>O<inf>3</inf>-based transmission gate memristor comparator-based median filter (TG-MCBMF) for on-sensor image enhancement in biomedical imaging systems such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), designed using Verilog-A. The current system performs front-end noise suppression directly at the sensor output stage, effectively removing salt and pepper (SAP) noise that is introduced during signal acquisition from sensors. The denoised images were reconstructed in MATLAB, and performance was evaluated using quality assessment metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean absolute error (MAE). The proposed filter demonstrated superior performance compared to traditional methods such as adaptive median filter (AMF), switch median (SM), and threshold and weighted median filter (TWMF), achieving PSNR values of 46.36 dB for brain CT and 43.84 dB for COVID-19 X-ray, alongside reduced MSE and MAE values of 1.5 and 29.53 for brain CT and 2.67 and 43.84 for COVID-19 X-ray, respectively. The findings indicate the potential of memristor-based filters for next-generation biomedical sensors. © 2017 IEEE.