Weighted aggregation for guided image filtering
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As a local filter, the guided image filtering (GIF) suffers from halo artifacts. To address this issue, a novel weighted aggregating strategy is proposed in this paper. By introducing the weighted aggregation to GIF, the proposed method called WAGIF can achieve results with sharp edges and avoid/reduce halo artifacts. More specifically, compared to the weighted guided image filtering and the gradient domain guided image filtering, the proposed method can achieve both fine and coarse smoothing results in the flat areas while preserving edges. In addition, the complexity of the proposed approach is O(N) for an image with N pixels. It is demonstrated that the GIF with weighted aggregation performs well in the fields of computational photography and image processing, including single image detail enhancement, tone mapping of high-dynamic-range images, single image haze removal, etc.
KeywordsEdge-preserving filtering Weighted aggregation Detail enhancement HDR tone mapping Haze removal
This work was supported in part by the National Natural Science Foundation of China under Grants 61775172 and 61371190. The authors wish to acknowledge the anonymous reviewers’ insightful and inspirational comments that have greatly helped to improve the technical contents and readability of this paper.
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