CNN based dark signal non-uniformity estimation

Image sensors come with a spatial inhomogeneity, known as Fixed Pattern Noise, that degrades the image quality. Especially the dark signal non uniformity (DSNU) component of the FPN drifts with time and depends highly on temperature and exposure time. In this paper we introduce a cellular neural network (CNN) to estimate the DSNU from a given set of recorded images. Therefore the foundations of a previously presented maximum likelihood estimation method are used. A rigorous mathematical derivation exploits the available sensor statistics and uses only well motivated statistical models to calculate the CNN's synaptic weights. The advantages of the resulting CNN-method are continuous DSNU updates and a reduction of the computational complexity. Furthermore, a comparison based on ground truth correction patterns shows a significant performance increase to related methods.