Multispectral image processing algorithms for enhanced vision systems in the Arctic
The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images being subject to contrast enhancement parameters are estimated. Based on these cha...
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| Veröffentlicht in: | IOP conference series. Earth and environmental science Jg. 302; H. 1; S. 12063 - 12070 |
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| Sprache: | Englisch |
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01.07.2019
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| Abstract | The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images being subject to contrast enhancement parameters are estimated. Based on these characteristics, the authors concluded that image areas requiring the greatest contrast enhancement are the areas with low saturation and magnitude gradients, at certain brightness values. Image quality improvement method is proposed. It performs processing only in the areas where it is necessary to enhance the contrast, practically without affecting the most homogeneous or structured image parts. The processed image saturation remains due to the processing of both luminance channel and saturation channel. The algorithm proposed also provides contrast enhancement of shaded image areas. The calculated values of various objective image quality indices indicate that the contrast enhancement algorithm proposed provides better results than known approaches. In addition, different spectral range image fusion algorithm ensuring visibility in the presence of interfering factors is proposed. It differs from known methods by adaptive weight adjustment in different areas of image. The example confirming the effectiveness of the fusion method proposed is shown. For its comparison with known methods, the values of fusion objective quality indices are calculated. The fusion algorithm proposed is shown to surpass known methods by various quality assessments. The conclusion about the expediency of using the algorithms developed in technical vision systems of robotic complexes in the Arctic is made. |
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| AbstractList | The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images being subject to contrast enhancement parameters are estimated. Based on these characteristics, the authors concluded that image areas requiring the greatest contrast enhancement are the areas with low saturation and magnitude gradients, at certain brightness values. Image quality improvement method is proposed. It performs processing only in the areas where it is necessary to enhance the contrast, practically without affecting the most homogeneous or structured image parts. The processed image saturation remains due to the processing of both luminance channel and saturation channel. The algorithm proposed also provides contrast enhancement of shaded image areas. The calculated values of various objective image quality indices indicate that the contrast enhancement algorithm proposed provides better results than known approaches. In addition, different spectral range image fusion algorithm ensuring visibility in the presence of interfering factors is proposed. It differs from known methods by adaptive weight adjustment in different areas of image. The example confirming the effectiveness of the fusion method proposed is shown. For its comparison with known methods, the values of fusion objective quality indices are calculated. The fusion algorithm proposed is shown to surpass known methods by various quality assessments. The conclusion about the expediency of using the algorithms developed in technical vision systems of robotic complexes in the Arctic is made. |
| Author | Skonnikov, P N Baukov, A A Kirillov, S N Pokrovskij, P S |
| Author_xml | – sequence: 1 givenname: S N surname: Kirillov fullname: Kirillov, S N email: kirillov.lab@mail.ru organization: Ryazan State Radio Engineering University , Russia – sequence: 2 givenname: P S surname: Pokrovskij fullname: Pokrovskij, P S email: paulps@list.ru organization: Ryazan State Radio Engineering University , Russia – sequence: 3 givenname: A A surname: Baukov fullname: Baukov, A A email: baukov.andrej@yandex.ru organization: Ryazan State Radio Engineering University , Russia – sequence: 4 givenname: P N surname: Skonnikov fullname: Skonnikov, P N email: skonnikovpn@yandex.ru organization: Ryazan State Radio Engineering University , Russia |
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| Cites_doi | 10.1049/el:20000267 10.1109/TIP.2003.819861 10.18287/2412-6179-2017-41-6-957-962 10.1016/j.inffus.2006.09.001 10.1016/B978-0-12-336156-1.50061-6 10.18287/2412-6179-2016-40-2-266-274 10.1007/s00138-012-0416-6 |
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| References | Fisenko (EES_302_1_012063bib3) 2012; 3 Jia (EES_302_1_012063bib4) 2012; 23 Drynkin (EES_302_1_012063bib13) 2012; 9 Kholopov (EES_302_1_012063bib1) 2016; 40 Insarov (EES_302_1_012063bib7) 2014; 3 Bondarenko (EES_302_1_012063bib8) 2016; 1 Asatryan (EES_302_1_012063bib6) 2017; 41 Xydeas (EES_302_1_012063bib10) 2000; 36 Yang (EES_302_1_012063bib14) 2008; 9 Wang (EES_302_1_012063bib9) 2004; 13 Haghighat (EES_302_1_012063bib11) 2014 Zuiderveld (EES_302_1_012063bib2) 1994; 4 Gonzalez (EES_302_1_012063bib5) 2012 EES_302_1_012063bib12 |
| References_xml | – volume: 36 start-page: 308 year: 2000 ident: EES_302_1_012063bib10 article-title: Objective image fusion performance measure publication-title: Electronics letters doi: 10.1049/el:20000267 – volume: 3 start-page: 294 year: 2012 ident: EES_302_1_012063bib3 article-title: Research and development of improved underwater images methods publication-title: X International Conf. “Applied Optics - 2012” transactions – volume: 1 start-page: 64 year: 2016 ident: EES_302_1_012063bib8 article-title: Evaluation of the informativeness of the combined images in multispectral vision systems publication-title: Software systems and computational methods – volume: 13 start-page: 600 year: 2004 ident: EES_302_1_012063bib9 article-title: Image quality assessment: from error visibility to structural similarity publication-title: IEEE transactions on image processing doi: 10.1109/TIP.2003.819861 – start-page: 1104 year: 2012 ident: EES_302_1_012063bib5 – volume: 41 start-page: 957 year: 2017 ident: EES_302_1_012063bib6 article-title: Image blurriness degree evaluation by the gradient field analysis publication-title: Computer optics doi: 10.18287/2412-6179-2017-41-6-957-962 – volume: 9 start-page: 156 year: 2008 ident: EES_302_1_012063bib14 article-title: A novel similarity based quality metric for image fusion publication-title: Information Fusion doi: 10.1016/j.inffus.2006.09.001 – volume: 4 start-page: 474 year: 1994 ident: EES_302_1_012063bib2 article-title: Contrast limited adaptive histogram equalization publication-title: Graphics gems doi: 10.1016/B978-0-12-336156-1.50061-6 – volume: 40 start-page: 266 year: 2016 ident: EES_302_1_012063bib1 article-title: Color image generation algorithm from monochrome visible and long-wave infrared video sensors in the YCbCr color space implementation publication-title: Computer optics doi: 10.18287/2412-6179-2016-40-2-266-274 – ident: EES_302_1_012063bib12 – volume: 3 start-page: 1 year: 2014 ident: EES_302_1_012063bib7 article-title: Problems of constructing technical vision systems using fusion of information channels of various spectral ranges publication-title: Information Technologies – start-page: 1 year: 2014 ident: EES_302_1_012063bib11 article-title: A Fast-FMI: non-reference image fusion metric – volume: 23 start-page: 1059 year: 2012 ident: EES_302_1_012063bib4 article-title: A two-step approach to see-through bad weather for surveillance video quality enhancement publication-title: Machine Vision and Applications doi: 10.1007/s00138-012-0416-6 – volume: 9 start-page: 33 year: 2012 ident: EES_302_1_012063bib13 article-title: Formation of a combined image in a two-zone onboard aerospace system publication-title: Mechanics, control and computer science |
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