De-ghosting of HDR images with double-credit intensity mapping
Ghosting artifacts are usually caused by moving object when composing a high dynamic range image from multiple differently exposed conventional images. In this paper, a robust de-ghosting algorithm is proposed based on a double-credit intensity mapping function (IMF) and an adaptive threshold model derived from statistical training. The double-credit IMF is estimated using both pixel intensity distribution and spatial correlation. A statistical threshold model is trained from the image database, and the key parameters are determined on the fly with variance vector calculated during the IMF estimation to adapt to different scenarios. Optimal bidirectional comparison is used for further improves the detection accuracy. The experiments show the effectiveness of the proposed de-ghosting method.