SMARTFILTER: MACHINE LEARNING-BASED IMAGE ENHANCEMENT FOR IR AND VISIBLE SENSORS

Abstract

Infrared (IR) and visible sensor images are critical in applications such as surveillance, remote sensing, and autonomous navigation. However, these images often suffer from noise, low contrast, and resolution inconsistencies, which can degrade analysis and interpretation. This study presents SmartFilter, a machine learning-based anisotropic filtering framework designed to enhance the quality of IR and visible sensor images. The approach leverages adaptive learning to preserve edges and fine details while reducing noise and artifacts. By training on multimodal sensor datasets, SmartFilter automatically adjusts filtering parameters to optimize image clarity and contrast for diverse scenarios. Experimental results demonstrate significant improvements in signal-to-noise ratio, edge preservation, and visual quality compared to traditional anisotropic filtering techniques, making it suitable for advanced sensor-based applications.