Gradient Domain Guided Image Filtering.

Abstract

Guided image filter (GIF) is a well-known local filter for its edge-preserving property and low computational complexity. Unfortunately, the GIF may suffer from halo artifacts, because the local linear model used in the GIF cannot represent the image well near some edges. In this paper, a gradient domain GIF is proposed by incorporating an explicit first-order edge-aware constraint. The edge-aware constraint makes edges be preserved better. To illustrate the efficiency of the proposed filter, the proposed gradient domain GIF is applied for single-image detail enhancement, tone mapping of high dynamic range images and image saliency detection. Both theoretical analysis and experimental results prove that the proposed gradient domain GIF can produce better resultant images, especially near the edges, where halos appear in the original GIF.