Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement

Yingkun Hou; Xiaobo Qu; Guanghai Liu; Seong Whan Lee; Dinggang Shen

doi:10.1155/2019/6395147

Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement

Yingkun Hou, Xiaobo Qu, Guanghai Liu, Seong Whan Lee, Dinggang Shen

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.

Original language	English
Article number	6395147
Journal	Mathematical Problems in Engineering
Volume	2019
DOIs	https://doi.org/10.1155/2019/6395147
Publication status	Published - 2019

ASJC Scopus subject areas

Mathematics(all)
Engineering(all)

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@article{f1d48810aaf640cfadaf0d20395ed3c8,

title = "Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement",

abstract = "In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.",

author = "Yingkun Hou and Xiaobo Qu and Guanghai Liu and Lee, {Seong Whan} and Dinggang Shen",

note = "Funding Information: This work was supported by the National Science Foundation of China [grant numbers 61379015 and 61866005]; the Natural Science Foundation of Shandong Province [grant number ZR2011FM004]; and the Talent Introduction Project of Taishan University [grant numbers Y-01-2013012 and Y-01-2014018]; Dr. S.-W. Lee was partially supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451).",

year = "2019",

doi = "10.1155/2019/6395147",

language = "English",

volume = "2019",

journal = "Mathematical Problems in Engineering",

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publisher = "Hindawi Publishing Corporation",

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T1 - Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement

AU - Hou, Yingkun

AU - Qu, Xiaobo

AU - Liu, Guanghai

AU - Lee, Seong Whan

AU - Shen, Dinggang

N1 - Funding Information: This work was supported by the National Science Foundation of China [grant numbers 61379015 and 61866005]; the Natural Science Foundation of Shandong Province [grant number ZR2011FM004]; and the Talent Introduction Project of Taishan University [grant numbers Y-01-2013012 and Y-01-2014018]; Dr. S.-W. Lee was partially supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451).

PY - 2019

Y1 - 2019

N2 - In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.

AB - In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.

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