Energy efficient in situ memristor-based image stabilization system

While image stabilization is crucial for enhancing unmanned aerial vehicle (UAV) performance in military, civilian, and disaster scenarios, conventional approaches suffer from analog-to-digital conversion bottlenecks and von Neumann architecture constraints. This study presents the first implementation of image stabilization technology using memristor arrays, addressing critical limitations of traditional methods in energy efficiency and latency for UAV applications. We propose an in situ memristor-based stabilization system that bypasses digital conversion by in situ executing grayscale projection and displacement detection algorithms directly in the analog domain via 8-kb 1T1R memristor arrays. The in situ system leverages in-memory computing to eliminate data migration overhead while enabling two types of image stabilization: offset data are input as voltage, and offset data are stored in situ as conductance. Experimental validation involved two scenarios: small-scale binary image sequences (6 × 6 pixels, 100 frames) and large-scale remote sensing images (700 × 700 pixels, ten originals with 100 offset samples). The results demonstrated pixel stabilization accuracy and indicated significant advantages in terms of speed (×403.65) and energy consumption (×1.57) when compared to conventional CMOS systems. This breakthrough establishes memristor-based stabilization as a transformative solution for high-speed, low-power UAV imaging systems, particularly under dynamic operational conditions.