Multi-Sensor Image Fusion for Enhanced Resolution and Feature Detection in Automotive and Medical Applications

Abstract

Multi-sensor image fusion represents a transformative advancement in imaging technology with significant implications for both automotive safety systems and medical diagnostic applications. By integrating multiple camera sensors with different spectral sensitivities and implementing scene-specific exposure controls, this technology addresses fundamental constraints of traditional single-sensor imaging in challenging visual environments. The integration of RGBIR (Red, Green, Blue, and Infrared) sensors particularly extends imaging capabilities into low-light conditions, crucial for automotive navigation in darkness and visualization of subsurface structures in medical endoscopy. Through a hierarchical wavelet decomposition mechanism, the system fuses multi-spectral and multi-exposure inputs while maintaining computational efficiency. Experimental implementations in both automotive and medical contexts demonstrate substantial improvements in object detection, tissue differentiation, and operational reliability across diverse environmental conditions. The adaptive exposure control system effectively normalizes contrast and brightness across scenes with extreme illumination ranges, while the infrared channel provides complementary information invisible to conventional RGB imaging. This technology shows particular promise for safety-critical applications requiring reliable visual information in suboptimal lighting conditions, offering a comprehensive solution to longstanding challenges in machine vision systems.