Noise-adaptive spatio-temporal filter for real-time noise removal in low light level images

Noise reduction gradually becomes one of the most important features in consumer cameras. The video signal is easily interfered by noise during acquisition process especially in low light environment. Many of the state-of-the-art filters for noise reduction perform-well for high contrast images. However, for low light images, the filter performance degrades seriously. In this paper, we propose a noise-adaptive spatio-temporal (NAST) filtering for removal of noise in low light level images. The proposed algorithm consists of a statistical domain temporal filter (SDTF) for moving area and a spatial hybrid filter (SHF) for stationary area. By minimizing required resources for implementation, we present a high quality, low-cost noise reduction filter for low light images. Since the proposed algorithm is designed for real-time implementation, it can be used as a pre-filter for a DCT-based encoder to enhance the coding efficiency of many commercial applications such as low cost camcorders, digital cameras, CCTV, and surveillance video systems.