A software method to extend tonal levels and widen tonal range of CCD sensor images

As one of important outcomes of the past decades of researches on sensor arrays for digital cameras, the manufacturers of sensor array technology have responded to the necessity and importance of obtaining an optimal fill factor, which has great impact on collection of incident photons on the sensor, with hardware solution e.g. by introducing microlenses. However it is still impossible to make a fill factor of 100% due to the physical limitations in practical development and manufacturing of digital camera. This has been a bottle neck problem for improving dynamic range and tonal levels for digital cameras e.g. CCD cameras. In this paper we propose a software method to not only widen the recordable dynamic range of a captured image by a CCD camera but also extend its tonal levels. In the method we estimate the fill factor and by a resampling process a virtual fill factor of 100% is achieved where a CCD image is rearranged to a new grid of virtual subpixels. A statistical framework including local learning model and Bayesian inference is used for estimating new sub-pixel intensity values. The highest probability of sub-pixels intensity values in each resampled pixel area is used to estimate the pixel intensity values of the new image. The results show that in comparison to the methods of histogram equalization and image contrast enhancement, which are generally used for improving the displayable dynamic range on only one image, the tonal levels and dynamic range of the image is extended and widen significantly and respectively.