Real-time edge-aware weighted median filtering on the GPU

Abstract

Edge-preserving weighted median filtering is a fundamental operator in a great variety of image processing and computer graphics applications. This paper presents a novel real-time weighted median filter which effectively smoothes out high-frequency details while preserving major edges. We propose an edge-aware data structure, the 4-dimensional bilateral grid, by extending the 3-dimensional bilateral grid with an additional range dimension. The edge-aware weights and the weighted median values are computed in the 4-dimensional space. The proposed algorithm is highly parallel, enabling a real-time GPU-based implementation. Experimental results show that our filter can be run in real-time on modern GPUs. A variety of edge-aware applications, including detail enhancement, inverse halftoning, JPEG artifact removal, image stylization, photographic look transfer and image colorization, are demonstrated to verify the feasibility of the proposed weighted median filter.