Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing

TL;DR

This paper introduces two local activity-tuned filtering frameworks that improve noise removal and image smoothing by utilizing local variance and standard deviation measurements, enhancing performance across various image processing tasks.

Contribution

The paper proposes novel local activity-tuned filtering frameworks, including a modified anisotropic diffusion and a LAT-RTV method, for improved noise removal and image smoothing.

Findings

Effective noise removal in piece-wise smooth images

Enhanced image smoothing with local activity measurement

Demonstrated improvements in depth image filtering and artifact removal

Abstract

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.