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

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

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Publisher Copyright:
© 2019 Yingkun Hou et al.

ASJC Scopus subject areas

General Mathematics
General Engineering

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10.1155/2019/6395147

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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",

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year = "2019",

doi = "10.1155/2019/6395147",

language = "English",

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journal = "Mathematical Problems in Engineering",

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AU - Hou, Yingkun

AU - Qu, Xiaobo

AU - Liu, Guanghai

AU - Lee, Seong Whan

AU - Shen, Dinggang

PY - 2019

Y1 - 2019

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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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JO - Mathematical Problems in Engineering

JF - Mathematical Problems in Engineering

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ER -

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

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