Feature and noise adaptive unsharp masking based on statistical hypotheses test

The conventional unsharp masking (UM) enhances the visual appearances of images by adding their amplified high frequency components. However, the noise component of the input image also tends to be amplified due to the nature of the UM. Hence, the application of the conventional UM is not suitable when noise is present. This paper exploits the statistical theories proposed in A. Polesel, et al., (1997) and Y.-H. Kim and J. Lee, (Nov 2005) for detecting noise and image feature of the input image so that the UM could be adaptively applied accordingly. By applying the proposed algorithm, it is made possible to enhance local contrast of the image, especially, the area with small details, without boosting up the noise counterpart. This results in natural looking output image.