Fast Guided Median Filter.

Faster computation of a weighted median (WM) filter is impeded by the construction of a weighted histogram for every local window of data. Since the calculated weights vary for each local window, it is difficult, using a sliding window approach, to construct the weighted histogram efficiently. In this paper, we propose a novel WM filter that overcomes the difficulty of histogram construction. Our proposed method achieves real-time processing for higher resolution images and can be applied to multidimensional, multichannel, and high precision data. The weight kernel used in our WM filter is the pointwise guided filter, which is derived from the guided filter. The use of kernels based on the guided filter avoids gradient reversal artifacts and shows a higher denoising performance than the Gaussian kernel based on the color/intensity distance. The core idea of the proposed method is a formulation that allows the use of histogram updates with a sliding window approach to find the weighted median. For high precision data we propose an algorithm based on a linked list that can reduce the memory requirements of storing histograms and the computational cost of updating them. We present implementations of the proposed method that are suitable for both CPU and GPU. Experimental results show that the proposed method indeed realizes faster computation than conventional WM filters and is capable of filtering multidimensional, multichannel, and high precision data. This is an approach which is difficult to achieve with conventional methods. [ABSTRACT FROM AUTHOR]