An illuminance-reflectance nonlinear video enhancement model for homeland security applications

Abstract

A illuminance-reflectance model based video stream enhancement algorithm is proposed for improving the visual quality of digital video streams captured by surveillance camera under insufficient and/or nonuniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. The images are processed in a similar way as human eyes sensing a scene. The algorithm demonstrates high quality of enhanced images, robust performance and fast processing speed. Compared with Retinex and multi-scale retinex with color restoration (MSRCR), the proposed method shows a better balance between luminance enhancement and contrast enhancement as well as a more consistent and reliable color rendition without introducing incorrect colors. This is an effective technique for image enhancement with simple computational procedures, which makes real-time enhancement for homeland security application successfully realized. The application of this image enhancement technique to the FRGC images yields improved face recognition results