Image Processing Framework for Performance Enhancement of Low-Light Image Sensors

Abstract

Night vision devices are used for capturing images under low light conditions ( $ lux), which are significantly different from images under standard illumination. The night vision devices based on image intensifier tube have power and cost advantages over other night vision devices, such as thermal imaging for short-range surveillance. However, these vision devices suffer from scintillation noise, which makes the resulting images noisy under very low light conditions. This paper presents a comprehensive methodology that uses a consolidated image processing module to improve the performance of low-light images acquired using image-intensifier-based night vision devices. The proposed module is a combination of a two-stage image processing framework. First, a spatio-temporal filter (based on wavelet and recursive frame averaging) is used to minimize inter-frame and intra-frame noise. Then, image enhancement is performed followed by residual noise reduction using a local spatial filter before applying image tone adjustment for display mapping. The results of the proposed module demonstrate performance enhancement of night vision devices under controlled simulated light conditions as well as field conditions. Objective and subjective analysis also reveals that the proposed module can effectively reduce noise and significantly improve visual image quality by 20–30%.