Blurriness-guided unsharp masking
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 b...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2019
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| Online Access: | https://hdl.handle.net/10356/103581 http://hdl.handle.net/10220/48595 |
| _version_ | 1811691574705258496 |
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| author | Ye, Wei Ma, Kai-Kuang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ye, Wei Ma, Kai-Kuang |
| author_sort | Ye, Wei |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:22:04Z |
| format | Journal Article |
| id | ntu-10356/103581 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:22:04Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1035812020-03-07T14:00:37Z Blurriness-guided unsharp masking Ye, Wei Ma, Kai-Kuang School of Electrical and Electronic Engineering Image Enhancement Unsharp Masking DRNTU::Engineering::Electrical and electronic engineering 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. Accepted version 2019-06-07T03:13:13Z 2019-12-06T21:15:54Z 2019-06-07T03:13:13Z 2019-12-06T21:15:54Z 2018 Journal Article 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 1057-7149 https://hdl.handle.net/10356/103581 http://hdl.handle.net/10220/48595 10.1109/TIP.2018.2838660 en IEEE Transactions on Image Processing © 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. 13 p. application/pdf |
| spellingShingle | Image Enhancement Unsharp Masking DRNTU::Engineering::Electrical and electronic engineering Ye, Wei Ma, Kai-Kuang Blurriness-guided unsharp masking |
| title | Blurriness-guided unsharp masking |
| title_full | Blurriness-guided unsharp masking |
| title_fullStr | Blurriness-guided unsharp masking |
| title_full_unstemmed | Blurriness-guided unsharp masking |
| title_short | Blurriness-guided unsharp masking |
| title_sort | blurriness guided unsharp masking |
| topic | Image Enhancement Unsharp Masking DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/103581 http://hdl.handle.net/10220/48595 |
| work_keys_str_mv | AT yewei blurrinessguidedunsharpmasking AT makaikuang blurrinessguidedunsharpmasking |