Intensity image denoising for laser active imaging system using nonsubsampled contourlet transform and SURE approach
This paper presents an algorithm based on nonsubsampled contourlet transform (NSCT) and Stein's unbiased risk estimate with a linear expansion of thresholds (SURE-LET) approach for intensity image denoising. First, we analyzed the multiplicative noise model of intensity image and make the non-l...
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| Veröffentlicht in: | Optik (Stuttgart) 2012-05, Vol.123 (9), p.808-813 |
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| container_title | Optik (Stuttgart) |
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| creator | Li, Xiao feng Xu, Jun Luo, Jijun Cao, Lijia Zhang, Shengxiu |
| description | This paper presents an algorithm based on nonsubsampled contourlet transform (NSCT) and Stein's unbiased risk estimate with a linear expansion of thresholds (SURE-LET) approach for intensity image denoising. First, we analyzed the multiplicative noise model of intensity image and make the non-logarithmic transform on the noisy signal. Then, as a multiscale geometric representation tool with multi-directivity and shift-invariance, NSCT was performed to capture the geometric information of images. Finally, SURE-LET strategy was modified to minimize the estimation of the mean square error between the clean image and the denoised one in the NSCT domain. Experiments on real intensity images show that the algorithm has excellent denoising performance in terms of the peak signal-to-noise ratio (PSNR), the computation time and the visual quality. |
| doi_str_mv | 10.1016/j.ijleo.2011.06.042 |
| format | Article |
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First, we analyzed the multiplicative noise model of intensity image and make the non-logarithmic transform on the noisy signal. Then, as a multiscale geometric representation tool with multi-directivity and shift-invariance, NSCT was performed to capture the geometric information of images. Finally, SURE-LET strategy was modified to minimize the estimation of the mean square error between the clean image and the denoised one in the NSCT domain. 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Experiments on real intensity images show that the algorithm has excellent denoising performance in terms of the peak signal-to-noise ratio (PSNR), the computation time and the visual quality.</description><subject>Algorithms</subject><subject>Estimates</subject><subject>Image denoising</subject><subject>Imaging</subject><subject>Intensity image</subject><subject>Laser Active imaging system</subject><subject>Nonsubsampled contourlet transform</subject><subject>Risk</subject><subject>Shape</subject><subject>Stein's unbiased risk estimate (SURE) minimization</subject><subject>Transforms</subject><issn>0030-4026</issn><issn>1618-1336</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNp9kD1v2zAQhokiBeqk_QVdOHaRehRlSh46FEGaBAgQIElngiaPKQ2JdHlUAP_70nbnTDfc897Hw9hXAa0Aob7v2rCbMLUdCNGCaqHvPrCVUGJshJTqgq0AJDQ9dOoTuyTaAcAwwLBi5T4WjBTKgYfZvCJ3GFOgEF-5T5lPhjBzY0t4wxNwbNCBCs58OVExRVq2ZOb9hI7bFEta8oSFl2wi1RkzN9Hx599PN9zs9zkZ--cz--jNRPjlf71iL79uXq7vmofH2_vrnw-NrUeXRq7N0G2lVw686TduBCW3pu_Qr0XvwErlhEM7bCRsvVvjBvzGODtI5UfpBnnFvp3H1q1_F6Si50AWp8lETAtpAV03jlXKEZVn1OZElNHrfa7v5kOF9FGx3umTYn1UrEHpqrimfpxTWJ94C5g12YDRogsZbdEuhXfz_wAQyIj8</recordid><startdate>201205</startdate><enddate>201205</enddate><creator>Li, Xiao feng</creator><creator>Xu, Jun</creator><creator>Luo, Jijun</creator><creator>Cao, Lijia</creator><creator>Zhang, Shengxiu</creator><general>Elsevier GmbH</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>201205</creationdate><title>Intensity image denoising for laser active imaging system using nonsubsampled contourlet transform and SURE approach</title><author>Li, Xiao feng ; Xu, Jun ; Luo, Jijun ; Cao, Lijia ; Zhang, Shengxiu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c336t-35a72b3f6d0fa49d8063ba42ef514d0c36d1dec7930bfd5e90f9adc736f83d73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Estimates</topic><topic>Image denoising</topic><topic>Imaging</topic><topic>Intensity image</topic><topic>Laser Active imaging system</topic><topic>Nonsubsampled contourlet transform</topic><topic>Risk</topic><topic>Shape</topic><topic>Stein's unbiased risk estimate (SURE) minimization</topic><topic>Transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xiao feng</creatorcontrib><creatorcontrib>Xu, Jun</creatorcontrib><creatorcontrib>Luo, Jijun</creatorcontrib><creatorcontrib>Cao, Lijia</creatorcontrib><creatorcontrib>Zhang, Shengxiu</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Optik (Stuttgart)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xiao feng</au><au>Xu, Jun</au><au>Luo, Jijun</au><au>Cao, Lijia</au><au>Zhang, Shengxiu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intensity image denoising for laser active imaging system using nonsubsampled contourlet transform and SURE approach</atitle><jtitle>Optik (Stuttgart)</jtitle><date>2012-05</date><risdate>2012</risdate><volume>123</volume><issue>9</issue><spage>808</spage><epage>813</epage><pages>808-813</pages><issn>0030-4026</issn><eissn>1618-1336</eissn><abstract>This paper presents an algorithm based on nonsubsampled contourlet transform (NSCT) and Stein's unbiased risk estimate with a linear expansion of thresholds (SURE-LET) approach for intensity image denoising. 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| ispartof | Optik (Stuttgart), 2012-05, Vol.123 (9), p.808-813 |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Estimates Image denoising Imaging Intensity image Laser Active imaging system Nonsubsampled contourlet transform Risk Shape Stein's unbiased risk estimate (SURE) minimization Transforms |
| title | Intensity image denoising for laser active imaging system using nonsubsampled contourlet transform and SURE approach |
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