Switching dual kernels for separable edge-preserving filtering

In this paper, we propose an accurate approximation framework for separable edge-preserving filtering. Naïve implementation of edge-preserving filtering, such as bilateral filtering and non-local means filtering, consumes enormous computational costs. Separable implementation of such filters is an efficient approximation method for real-time filtering. The accuracy of the conventional separable representation, however, is inadequate when the kernel size is immense. To improve the accuracy, we prepare dual kernels that have different kernel weights for horizontal and vertical filtering of separable filtering. In the experiment, we validate the proposed implementation by using three kinds of filters; bilateral filtering, dual bilateral filtering, and non-local means filtering. Experimental results show that the proposed implementation has higher accuracy while the computational time is almost the same. Moreover, the proposed implementation is practical for denoising and disparity map refinement applications.