LAUSR

An image after tone mapping (TM) has noise bias, i.e., noise values with a non-zero mean, because of the non-linearity of the TM function. Therefore, noise reduction filters based on the zero-mean assumption do not work well for such images. To overcome this limitation, noise bias compensation (NBC) divides pixels into subsets depending on their values and adaptively adjusts them using a Bayesian approach. However, previous studies on NBC target only gray-scale images and assume that the noise mean before TM is zero. This paper proposes a method for NBC that targets color images processed by TM with a non-zero noise mean. The proposed method adaptively calculates the compensation values based on prior knowledge that represents noise corresponding to each pixel value of RGB channels with a Bayesian approach. Experimental results show this Bayesian approach successfully reduces noise even for color images containing noise with a non-zero mean.