Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/653028
Title: Guided Image Contrast Enhancement Based on Retrieved Images in Cloud
Authors: Shiqi Wang;Ke Gu;Siwei Ma;Weisi Lin;Xianming Liu;Wen Gao
subject: surface quality|sigmoid transfer mapping|image quality assessment|Contrast enhancement|unsharp masking|free-energy|retrieved images
Year: 2016
Publisher: IEEE
Abstract: We propose a guided image contrast enhancement framework based on cloud images, in which the context- sensitive and context-free contrast is jointly improved via solving a multi-criteria optimization problem. In particular, the context-sensitive contrast is improved by performing advanced unsharp masking on the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. To automatically determine the contrast enhancement level, the parameters in the optimization process are estimated by taking advantages of the retrieved images with similar content. For the purpose of automatically avoiding the involvement of low-quality retrieved images as the guidance, a recently developed no-reference image quality metric is adopted to rank the retrieved images from the cloud. The image complexity from the free-energy-based brain theory and the surface quality statistics in salient regions are collaboratively optimized to infer the parameters. Experimental results confirm that the proposed technique can efficiently create visually-pleasing enhanced images which are better than those produced by the classical techniques in both subjective and objective comparisons.
URI: http://localhost/handle/Hannan/137496
http://localhost/handle/Hannan/653028
ISSN: 1520-9210
1941-0077
volume: 18
issue: 2
Appears in Collections:2016

Files in This Item:
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Title: Guided Image Contrast Enhancement Based on Retrieved Images in Cloud
Authors: Shiqi Wang;Ke Gu;Siwei Ma;Weisi Lin;Xianming Liu;Wen Gao
subject: surface quality|sigmoid transfer mapping|image quality assessment|Contrast enhancement|unsharp masking|free-energy|retrieved images
Year: 2016
Publisher: IEEE
Abstract: We propose a guided image contrast enhancement framework based on cloud images, in which the context- sensitive and context-free contrast is jointly improved via solving a multi-criteria optimization problem. In particular, the context-sensitive contrast is improved by performing advanced unsharp masking on the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. To automatically determine the contrast enhancement level, the parameters in the optimization process are estimated by taking advantages of the retrieved images with similar content. For the purpose of automatically avoiding the involvement of low-quality retrieved images as the guidance, a recently developed no-reference image quality metric is adopted to rank the retrieved images from the cloud. The image complexity from the free-energy-based brain theory and the surface quality statistics in salient regions are collaboratively optimized to infer the parameters. Experimental results confirm that the proposed technique can efficiently create visually-pleasing enhanced images which are better than those produced by the classical techniques in both subjective and objective comparisons.
URI: http://localhost/handle/Hannan/137496
http://localhost/handle/Hannan/653028
ISSN: 1520-9210
1941-0077
volume: 18
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360203.pdf3.37 MBAdobe PDFThumbnail
Preview File
Title: Guided Image Contrast Enhancement Based on Retrieved Images in Cloud
Authors: Shiqi Wang;Ke Gu;Siwei Ma;Weisi Lin;Xianming Liu;Wen Gao
subject: surface quality|sigmoid transfer mapping|image quality assessment|Contrast enhancement|unsharp masking|free-energy|retrieved images
Year: 2016
Publisher: IEEE
Abstract: We propose a guided image contrast enhancement framework based on cloud images, in which the context- sensitive and context-free contrast is jointly improved via solving a multi-criteria optimization problem. In particular, the context-sensitive contrast is improved by performing advanced unsharp masking on the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. To automatically determine the contrast enhancement level, the parameters in the optimization process are estimated by taking advantages of the retrieved images with similar content. For the purpose of automatically avoiding the involvement of low-quality retrieved images as the guidance, a recently developed no-reference image quality metric is adopted to rank the retrieved images from the cloud. The image complexity from the free-energy-based brain theory and the surface quality statistics in salient regions are collaboratively optimized to infer the parameters. Experimental results confirm that the proposed technique can efficiently create visually-pleasing enhanced images which are better than those produced by the classical techniques in both subjective and objective comparisons.
URI: http://localhost/handle/Hannan/137496
http://localhost/handle/Hannan/653028
ISSN: 1520-9210
1941-0077
volume: 18
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7360203.pdf3.37 MBAdobe PDFThumbnail
Preview File