  In this paper, two local activity-tuned filtering frameworks are proposed for
noise removal and image smoothing, where the local activity measurement is
given by the clipped and normalized local variance or standard deviation. The
first framework is a modified anisotropic diffusion for noise removal of
piece-wise smooth image. The second framework is a local activity-tuned
Relative Total Variation (LAT-RTV) method for image smoothing. Both frameworks
employ the division of gradient and the local activity measurement to achieve
noise removal. In addition, to better capture local information, the proposed
LAT-RTV uses the product of gradient and local activity measurement to boost
the performance of image smoothing. Experimental results are presented to
demonstrate the efficiency of the proposed methods on various applications,
including depth image filtering, clip-art compression artifact removal, image
smoothing, and image denoising.
