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 journalArticlepeer-review


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 languageEnglish
Article number6395147
JournalMathematical Problems in Engineering
Publication statusPublished - 2019

Bibliographical 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).

Publisher Copyright:
© 2019 Yingkun Hou et al.

ASJC Scopus subject areas

  • General Mathematics
  • General Engineering


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