journal article·2018-03-01Open access

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

Liangliang Li, Yujuan Si, Zhenhong Jia

AIP Advances· Volume 8· Issue 3

Abstract

In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Image Fusion Techniques · Image Enhancement Techniques · Image and Signal Denoising Methods

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Communication Engineering, Jilin University 1 , Changchun 130012, China

Department of Electronic Information, Zhuhai College of Jilin University 2 , Zhuhai 519041, China

College of Information Science and Engineering, Xinjiang University 3 , Urumqi 830046, China

Funding

the Innovation and Strengthening School Project (provincial key platform and major scientific research project) supported by Guangdong Government · the Key Scientific and Technological Project of Changchun Government · the Key Scientific and Technological Research Project of Jilin Province · the Premier-Discipline Enhancement Scheme Supported by Zhuhai Government and Premier Key-Discipline Enhancement Scheme Supported by Guangdong Government Funds

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