Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing
This work addresses image processing tasks such as noise removal and smoothing, but it appears incremental as it modifies existing methods like anisotropic diffusion and Relative Total Variation.
The paper tackled noise removal and image smoothing by proposing two local activity-tuned filtering frameworks, achieving efficient performance in applications like depth image filtering and denoising.
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.