Restricted guided filter with SURE-LET-based parameter optimization
Cuong Cao Pham, Jae Wook Jeon · 2012
Guided image filtering has recently emerged as an effective technique for noise reduction and edge-preserving smoothing operation due to its appealing properties. It outperforms conventional bilateral filtering in a variety of applications in terms of both quality and computational cost. However, the combination of smoothing parameters (ε and Ωs) that delivers optimal results has not yet been reported, and the contrast of the filtered output is considerably reduced. In this paper, we present a restricted version of guided filtering that has better contrast-preserving characteristics, and use Stein's unbiased risk estimate (SURE) with an exhaustive search or a linear expansion of threshold (LET) to optimally tune the two above parameters. With SURE, the mean squared error (MSE) can be unbiasedly estimated without the requirement of the noise-free image. Experiments verified the accuracy of the SURE derivation and its effectiveness with respect to providing a better trade off between two interrelated objectives - noise reduction and edge-preservation.