An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-scale Convolution
In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement,...
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| Veröffentlicht in: | IEEE access 2020-01, Vol.8, p.1-1 |
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| creator | Lu, Haoxiang Liu, Zhengbing Pan, Xipeng |
| description | In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons. |
| doi_str_mv | 10.1109/ACCESS.2020.3017499 |
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The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3017499</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Contrast enhancement ; Convolution ; Detail sharpening ; Equalization ; Genetic algorithms ; Histograms ; Image contrast ; Image enhancement ; Image reconstruction ; Infrared image ; Infrared imagery ; Multiscale convolution ; Variance</subject><ispartof>IEEE access, 2020-01, Vol.8, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-707fa51fad0882f16c482cc6f6762d94e4cf36d59f0883f154683243dd2cf5653</citedby><cites>FETCH-LOGICAL-c408t-707fa51fad0882f16c482cc6f6762d94e4cf36d59f0883f154683243dd2cf5653</cites><orcidid>0000-0003-2284-5154</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9170597$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27612,27903,27904,54911</link.rule.ids></links><search><creatorcontrib>Lu, Haoxiang</creatorcontrib><creatorcontrib>Liu, Zhengbing</creatorcontrib><creatorcontrib>Pan, Xipeng</creatorcontrib><title>An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-scale Convolution</title><title>IEEE access</title><addtitle>Access</addtitle><description>In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.</description><subject>Contrast enhancement</subject><subject>Convolution</subject><subject>Detail sharpening</subject><subject>Equalization</subject><subject>Genetic algorithms</subject><subject>Histograms</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Image reconstruction</subject><subject>Infrared image</subject><subject>Infrared imagery</subject><subject>Multiscale convolution</subject><subject>Variance</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUctKxDAULaKgqF_gJuC6Y95tlmMddWDEhboOd_LQDJ1mTFtBv96MFTFwSTj3PAKnKC4InhGC1dW8aRZPTzOKKZ4xTCqu1EFxQolUJRNMHv57Hxfnfb_B-dQZEtVJoecdmlvYDeHDoRs3QGjR4n2ENnzBEGKHfExo2fkEyVm03MKrQ4vuDTrjtq4b0DX0Gc-8h7EdQtkbaB1qYvcR23GvPyuOPLS9O_-9T4uX28Vzc1-uHu-WzXxVGo7roaxw5UEQDxbXNfVEGl5TY6SXlaRWcceNZ9IK5fOeeSK4rBnlzFpqvJCCnRbLyddG2OhdCltInzpC0D9ATK8a0hBM6_TaYPCqMoRJxa3ksBZA2Npj7GENqspel5PXLsX30fWD3sQxdfn7mvKcnIfwzGITy6TY98n5v1SC9b4YPRWj98Xo32Ky6mJSBefcn0KRCouc_A3Esoj4</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Lu, Haoxiang</creator><creator>Liu, Zhengbing</creator><creator>Pan, Xipeng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3017499</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-2284-5154</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Contrast enhancement Convolution Detail sharpening Equalization Genetic algorithms Histograms Image contrast Image enhancement Image reconstruction Infrared image Infrared imagery Multiscale convolution Variance |
| title | An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-scale Convolution |
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