Gradient Domain Guided Image Filtering

Gradient Domain Guided Image Filtering
Title:
Gradient Domain Guided Image Filtering
Other Titles:
IEEE Transactions on Image Processing
DOI:
10.1109/TIP.2015.2468183
Publication Date:
13 August 2015
Citation:
F. Kou, W. Chen, C. Wen and Z. Li, "Gradient Domain Guided Image Filtering," in IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4528-4539, Nov. 2015. doi: 10.1109/TIP.2015.2468183
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.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1057-7149
1941-0042
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