Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103581
Title: Blurriness-guided unsharp masking
Authors: Ye, Wei
Ma, Kai-Kuang
Keywords: Image Enhancement
Unsharp Masking
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Ye, W., & Ma, K.-K. (2018). Blurriness-guided unsharp masking. IEEE Transactions on Image Processing, 27(9), 4465-4477. doi:10.1109/TIP.2018.2838660
Series/Report no.: IEEE Transactions on Image Processing
Abstract: In this paper, a highly-adaptive unsharp masking (UM) method is proposed and called the blurriness-guided UM, or BUM, in short. The proposed BUM exploits the estimated local blurriness as the guidance information to perform pixel-wise enhancement. The consideration of local blurriness is motivated by the fact that enhancing a highly-sharp or a highly-blurred image region is undesirable, since this could easily yield unpleasant image artifacts due to over-enhancement or noise enhancement, respectively. Our proposed BUM algorithm has two powerful adaptations as follows. First, the enhancement strength is adjusted for each pixel on the input image according to the degree of local blurriness measured at the local region of this pixel's location. All such measurements collectively form the blurriness map, from which the scaling matrix can be obtained using our proposed mapping process. Second, we also consider the type of layer-decomposition filter exploited for generating the base layer and the detail layer, since this consideration would effectively help to prevent over-enhancement artifacts. In this paper, the layer-decomposition filter is considered from the viewpoint of edge-preserving type versus non-edge-preserving type. Extensive simulations experimented on various test images have clearly demonstrated that our proposed BUM is able to consistently yield superior enhanced images with better perceptual quality to that of using a fixed enhancement strength or other state-of-the-art adaptive UM methods.
URI: https://hdl.handle.net/10356/103581
http://hdl.handle.net/10220/48595
ISSN: 1057-7149
DOI: 10.1109/TIP.2018.2838660
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2018.2838660.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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