Солнечно-земная физика, 2017, том 3, № 2

СОЛНЕЧНО-ЗЕМНАЯ ФИЗИКА 

Свидетельство о регистрации  
средства массовой информации  
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 ISSN 2412-4737 
 DOI: 10.12737/issn. 2412-4737 
 Том 3, № 2, 2017. 75 с. 
 Выходит 4 раза в год

Учредители: Федеральное государственное бюджетное учреждение науки Институт солнечно-земной физики 
Сибирского отделения Российской академии наук 
Федеральное государственное бюджетное учреждение «Сибирское отделение Российской академии наук»

SOLAR-TERRESTRIAL PHYSICS 

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of mass media  
from January 29, 2014. ПИ № ФС77-56768 
The edition has been published since 1963 

  ISSN 2412-4737 
  DOI: 10.12737/issn. 2412-4737 
  Vol. 3, Iss. 2, 2017. 75 p. 
  Quarterly 

Founders: Institute of Solar-Terrestrial Physics of Siberian Branch of Russian Academy of Sciences

Siberian Branch of Russian Academy of Sciences

Состав редколлегии журнала 
Editorial Board 

Жеребцов Г.А., академик —
главный редактор, ИСЗФ СО РАН 

Zherebtsov G.A., Academician, Editor-in-Chief,  
ISTP SB RAS 

Степанов А.В., чл.-корр. РАН —
заместитель главного редактора, ГАО РАН 

Stepanov A.V., Corr. Member of RAS,  
Deputy Editor-in-Chief, GAO RAS 

Потапов А.С., д-р физ.-мат. наук —
заместитель главного редактора, ИСЗФ СО РАН

Potapov A.S., D.Sc. (Phys.&Math),  
Deputy Editor-in-Chief, ISTP SB RAS 

Члены редколлегии
Members of the Editorial Board  

Алтынцев А.Т., д-р физ.-мат. наук, ИСЗФ СО РАН
Белан Б.Д., д-р физ.-мат. наук, ИОА СО РАН 
Гульельми А.В., д-р физ.-мат. наук, ИФЗ РАН 
Деминов М.Г., д-р физ.-мат. наук, ИЗМИРАН 
Ермолаев Ю.И., д-р физ.-мат. наук, ИКИ РАН 
Лазутин Л.Л., д-р физ.-мат. наук, НИИЯФ МГУ
Леонович А.С., д-р физ.-мат. наук, ИСЗФ СО РАН
Максимов В.П., д-р физ.-мат. наук, ИСЗФ СО РАН
Мареев Е.А., чл.-корр. РАН, ИПФ РАН 
Мордвинов А.В., д-р физ.-мат. наук, ИСЗФ СО РАН
Обридко В.Н., д-р физ.-мат. наук, ИЗМИРАН 
Перевалова Н.П., д-р физ.-мат. наук, ИСЗФ СО РАН
Потехин А.П., чл.-корр. РАН, ИСЗФ СО РАН 
Салахутдинова И.И., канд. физ.-мат. наук,  
ученый секретарь, ИСЗФ СО РАН 
Сафаргалеев В.В., д-р физ.-мат. наук, ПГИ РАН
Сомов Б.В., д-р физ.-мат. наук, ГАИШ МГУ 
Стожков Ю.И., д-р физ.-мат. наук, ФИАН 
Тащилин А.В., д-р физ.-мат. наук, ИСЗФ СО РАН
Уралов А.М., д-р физ.-мат. наук, ИСЗФ СО РАН
Йихуа Йан, проф., Национальные астрономические 
обсерватории Китая, КАН, Пекин, Китай 
Лестер М., проф., Университет Лестера,  
Великобритания 

Altyntsev A.T., D.Sc. (Phys.&Math.), ISTP SB RAS
Belan B.D., D.Sc. (Phys.&Math.), IAO SB RAS 
Guglielmi A.V., D.Sc. (Phys.&Math.), IPE RAS 
Deminov M.G., D.Sc. (Phys.&Math.), IZMIRAN 
Yermolaev Yu.I., D.Sc. (Phys.&Math.), IKI RAS
Lazutin L.L., D.Sc. (Phys.&Math.), SINP MSU 
Leonovich A.S., D.Sc. (Phys.&Math.), ISTP SB RAS 
Maksimov V.P., D.Sc. (Phys.&Math.), ISTP SB RAS 
Mareev E.A., Corr. Member of RAS, IAP RAS 
Mordvinov A.V., D.Sc. (Phys.&Math.), ISTP SB RAS 
Obridko V.N., D.Sc. (Phys.&Math.), IZMIRAN 
Perevalova N.P., D.Sc. (Phys.&Math.), ISTP SB RAS 
Potekhin A.P., Corr. Member of RAS, ISTP SB RAS 
Salakhutdinova I.I., C.Sc. (Phys.&Math.),  
Scientific Secretary, ISTP SB RAS 
Safargaleev V.V., D.Sc. (Phys.&Math.), PGI KSC RAS 
Somov B.V., D.Sc. (Phys.&Math.), SAI MSU 
Stozhkov Yu.I., D.Sc. (Phys.&Math.), LPI RAS 
Tashchilin A.V., D.Sc. (Phys.&Math.), ISTP SB RAS 
Uralov A.M., D.Sc. (Phys.&Math.), ISTP SB RAS
Yihua Yan, Prof., National Astronomical Observatories, Beijing, China 
Lester M., Prof., University of Leicester, UK 

Панчева Дора, проф., Национальный институт геодезии,
геофизики и географии БАН, София, Болгария 

Pancheva D., Prof., Geophysical Institute, Bulgarian Academy 
of Sciences, Sofia, Bulgaria 

Полюшкина Н.А., ответственный секретарь редакции, 
ИСЗФ СО РАН 

Polyushkina N.A., Executive Secretary of Editorial Board,
ISTP SB RAS 

 
 
13-я РОССИЙСКО-КИТАЙСКАЯ 

КОНФЕРЕНЦИЯ 

ПО КОСМИЧЕСКОЙ ПОГОДЕ 

 
Материалы 

 
15–19 августа 2016 г. 

Якутск 

Институт космофизических исследований 

и аэрономии им. Ю.Г. Шафера  

(ИКФИА) 

 

13th RUSSIAN-CHINESE 

CONFERENCE 

ON SPACE WEATHER 

 
Proceedings 

 
August 15–19, 2016 

Yakutsk 

Yu.G. Shafer Institute 

of Cosmophysical Research and Aeronomy  

(ShICRA SB RAS) 

 
 
 
 

СОДЕРЖАНИЕ

Ганхуа Линь, Сяо-Фань Ван, Сяо Ян, Со Лю, Мэй Чжан, Хайминь Ван, Чан Лю, Янь Сюй, 
Тлатов А.Г., Демидов М.Л., Боровик А.В., Головко А.А. Формирование векового ряда данных по 
солнечной хромосфере для исследований, связанных с солнечной активностью…………………… 
5–9 

Лун Cюй, Йихуа Йан, Цзюнь Чэн. Направленная фильтрация для обработки изображений/видео
изображений Солнца ………………………………………………………………………. 10–17 

Гололобов П.Ю., Кривошапкин П.А., Крымский Г.Ф., Григорьев В.Г., Герасимова С.К. Распределение тензорной анизотропии космических лучей в окрестности нейтрального токового слоя….. 
18–21 

Гололобов П.Ю., Кривошапкин П.А., Крымский Г.Ф., Григорьев В.Г., Герасимова С.К. Ис
следование тензорной анизотропии космических лучей во время крупномасштабных возмущений
солнечного ветра……………………………………………………………………………………….. 
22–26 

Баишев Д.Г., Самсонов С.Н., Моисеев А.В., Бороев Р.Н., Степанов А.Е., Козлов В.И., Корсаков А.А., Торопов А.А., Йошикава А., Юмото К. Мониторинг и исследование эффектов космической погоды с помощью меридиональной цепочки инструментов в Якутии: краткий обзор……. 
27–35 

Моисеев А.В, Баишев Д.Г., Мишин В.В., Уозуми Т., Ешикава А., Ду А. Особенности формирования мелкомасштабных волновых возмущений во время резкого сжатия магнитосферы………. 36–44 

Ван Чжэн, Ши Цзянькуй, Ван Гоцзюнь, Ван Сяо, Жеребцов Г.А., Романова Е.Б., Ратов
ский К.Г., Полех Н.М. Суточные, сезонные, годовые и полугодовые вариации ионосферных параметров на разных широтах в восточно-азиатском секторе на фазе роста солнечной активности … 
 
45–53 

Гололобов А.Ю., Голиков И.А., Варламов И.И. Моделирование влияния магнитосферных потоков тепла на температуру электронов в субавроральной ионосфере…………………………… 
54–57 

Аммосова А.М., Гаврильева Г.А., Аммосов П.П., Колтовской И.И. Сравнение температуры 
субавроральной мезопаузы над Якутией с данными радиометра SABER с 2002 по 2014 г………… 
58–63 

Цзин Цзяо, Готао Ян, Цзихун Ван, Сюэу Чэн, Фацюнь Ли. Спорадические калиевые слои и 
их связь со спорадическими Е-слоями в области мезопаузы над Пекином (Китай)………………… 
64–69 

Тарабукина Л.Д., Козлов В.И. Пространственно-временное распределение грозовых разрядов 
по территории северного региона Азии и его сравнение с солнечной активностью в 2009–2016 гг. 
70–74 

CONTENTS 

Ganghua Lin, XiaoFan Wang, Xiao Yang, Suo Liu, Mei Zhang, Haimin Wang, Chang Liu, Yan 

Xu, Tlatov A.G., Demidov M.L., Borovik A.V., Golovko A.A. Construction of a century solar chromosphere data set for solar activity related research…………………………………………………………. 
5–9 

Long Xu, Yihua Yan, Jun Cheng. Guided filtering for solar image/video processing……………….. 
10–17

Gololobov P.Yu., Krivoshapkin P.A., Krymsky G.F., Grigoryev V.G., Gerasimova S.K. Distribution 
of tensor anisotropy of cosmic rays near the neutral current sheet…………………………………… 
18–21 

Gololobov P.Yu., Krivoshapkin P.A., Krymsky G.F., Grigoryev V.G., Gerasimova S.K. Investigating
tensor anisotropy of cosmic rays during large-scale solar wind disturbances……………………….. 
22–26 

Baishev D.G., Samsonov S.N., Moiseev A.V., Boroyev R.N., Stepanov A.E., Kozlov V.I., Korsakov A.A., Toropov A.A., Yoshikawa A., Yumoto K. Monitoring and investigating space weather effects 
with meridional chain of instruments in Yakutia: a brief overview ……………………………………… 
27–35 

Moiseev A.V., Baishev D.G., Mishin V.V., Uozumi T., Yoshikawa A., Du A. Features of formation of 
small-scale wave disturbances during a sudden magnetospheric compression………………………… 
36–44 

Wang Zheng, Shi Jiankui, Wang Guojun, Wang Xiao, Zherebtsov G.A., Romanova E.B., Ratov
sky K.G., Polekh N.M. Diurnal, seasonal, annual, and semi-annual variations of ionospheric parameters at 
different latitudes in East Asian sector during ascending phase of solar activity ……………………….. 
 
45–53 

Gololobov A.Yu., Golikov I.A., Varlamov I.I. Modeling the influence of magnetospheric heat fluxes 
on the electron temperature in the subauroral ionosphere………………………………………………. 
54–57 

Ammosova A.M., Gavrilyeva G.A., Ammosov P.P., Koltovskoi I.I. Сomparing temperature of subauroral mesopause over Yakutia with SABER radiometer data for 2002–2014……………………………… 58–63 

Jing Jiao, Guotao Yang, Jihong Wang, Xuewu Cheng, Faqun Li. Sporadic potassium layers and 
their connection to sporadic E layers in the mesopause region at Beijing, China…………………………. 
64–69 

Tarabukina L.D., Kozlov V.I. Spatial and temporal distribution of lightning strokes over North Asia 
and its comparison with solar activity variations in 2009–2016………………………………………….. 
70–74 

Солнечно-земная физика. 2017. Т. 3, № 2 
 
Solar-Terrestrial Physics. 2017. Vol. 3. Iss. 2 

5 

УДК 551.5:539.104(078)  
 
 
 
 
 
       Поступила в редакцию 15.11.2016 
DOI: 10.12737/22609  
 
 
 
 
 
 
       Принята к публикации 13.03.2017 

 
ФОРМИРОВАНИЕ ВЕКОВОГО РЯДА ДАННЫХ  
ПО СОЛНЕЧНОЙ ХРОМОСФЕРЕ ДЛЯ ИССЛЕДОВАНИЙ,  
СВЯЗАННЫХ С СОЛНЕЧНОЙ АКТИВНОСТЬЮ 
 
CONSTRUCTION OF A CENTURY SOLAR CHROMOSPHERE DATA SET  
FOR SOLAR ACTIVITY RELATED RESEARCH 
 
Ганхуа Линь 
Главная лаборатория исследования солнечной  
активности, Национальные астрономические  
обсерватории, Китайская академия наук,  
Пекин, Китай, lgh@nao.cas.cn 
Сяо-Фань Ван 
Главная лаборатория исследования солнечной  
активности, Национальные астрономические  
обсерватории, Китайская академия наук,  
Пекин, Китай 
Сяо Ян 
Главная лаборатория исследования солнечной  
активности, Национальные астрономические  
обсерватории, Китайская академия наук,  
Пекин, Китай 
Со Лю 
Главная лаборатория исследования солнечной  
активности, Национальные астрономические  
обсерватории, Китайская академия наук,  
Пекин, Китай 
Мэй Чжан 
Главная лаборатория исследования солнечной  
активности, Национальные астрономические  
обсерватории, Китайская академия наук,  
Пекин, Китай 
Хайминь Ван 
Лаборатория исследования космической погоды,  
Технологический институт Нью-Джерси,  
Ньюарк, США 
Чан Лю 
Лаборатория исследования космической погоды,  
Технологический институт Нью-Джерси,  
Ньюарк, США 
Янь Сюй 
Лаборатория исследования космической погоды,  
Технологический институт Нью-Джерси,  
Ньюарк, США 
А.Г. Тлатов 
Кисловодская горная астрономическая станция  
Пулковской обсерватории, РАН,  
Кисловодск, Россия 
М.Л. Демидов 
Институт солнечно-земной физики СО РАН,  
Иркутск, Россия 
А.В. Боровик 
Институт солнечно-земной физики СО РАН,  
Иркутск, Россия 
А.А. Головко 

Институт солнечно-земной физики СО РАН,  
Иркутск, Россия 
 

Ganghua Lin 
Key Laboratory of Solar Activity, National Astronomical  
Observatories, Chinese Academy of Sciences,  
Beijing, China, lgh@nao.cas.cn 

Xiao Fan Wang 
Key Laboratory of Solar Activity, National Astronomical  
Observatories, Chinese Academy of Sciences,  
Beijing, China 

Xiao Yang 
Key Laboratory of Solar Activity, National Astronomical  
Observatories, Chinese Academy of Sciences,  
Beijing, China 

Suo Liu 
Key Laboratory of Solar Activity, National Astronomical 
Observatories, Chinese Academy of Sciences,  
Beijing, China 

Mei Zhang 
Key Laboratory of Solar Activity, National Astronomical  
Observatories, Chinese Academy of Sciences,  
Beijing, China 

Haimin Wang 
Space Weather Research Laboratory, New Jersey Institute  
of Technology,  
Newark, USA 

Chang Liu 
Space Weather Research Laboratory, New Jersey Institute  
of Technology,  
Newark, USA 
Yan Xu 
Space Weather Research Laboratory, New Jersey Institute  
of Technology,  
Newark, USA 
A.G. Tlatov 
Kislovodsk Mountain Astronomical Station of the Pulkovo 
observatory, Russian Academy of Sciences,  
Kislovodsk, Russia 
M.L. Demidov  
Institute of Solar Terrestrial Physics SB RAS,  
Irkutsk, Russia  
A.V. Borovik  

Institute of Solar Terrestrial Physics SB RAS,  
Irkutsk, Russia Federation  
A.A. Golovko  

Institute of Solar Terrestrial Physics SB RAS,  
Irkutsk, Russia  

 

Ганхуа Линь, Сяо-Фань Ван, Сяо Ян, Со Лю и др.  
 
    Ganghua Lin, Xiao Fan Wang, Xiao Yang, Suo Liu, et al. 
 

6 

Аннотация. В статье представлен наш действующий проект «Формирование векового ряда данных 
по солнечной хромосфере для исследований, связанных с солнечной активностью». Солнечная активность является главным фактором космической 
погоды, влияющим на жизнь человечества. Некоторые серьезные последствия воздействия космической погоды, например, нарушение космической 
связи и навигации, угроза безопасности астронавтов 
и спутников, повреждение электрических систем. 
Поэтому исследование солнечной активности имеет 
и научный, и социальный аспекты. Основная база 
данных формируется из оцифрованных и нормированных данных, полученных в нескольких обсерваториях по всему земному шару, и покрывает более 
чем 100-летний временной интервал. После тщательной калибровки мы сможем извлечь и получить 
данные и вместе с полной базой данных предоставить их астрономическому сообществу. Нашей конечной целью является привлечение внимания к 
нескольким физическим проблемам: поведение волокон в солнечном цикле, аномальный ход 24 цикла, 
крупномасштабные солнечные эрупции и дистанционно-индуцированные 
уярчения. 
Существенный 
прогресс ожидается в разработке алгоритмов получения данных и программного обеспечения, что поможет научному анализу и в итоге будет способствовать пониманию солнечных циклов. 
 
Ключевые слова: солнечный цикл, Hα, волокно, 
многопараметрическая калибровка, нормирование, 
извлечение деталей, картина солнечной активности. 

Abstract. This article introduces our ongoing project “Construction of a Century Solar Chromosphere 
Data Set for Solar Activity Related Research”. Solar 
activities are the major sources of space weather that 
affects human lives. Some of the serious space weather 
consequences, for instance, include interruption of space 
communication and navigation, compromising the safety of astronauts and satellites, and damaging power 
grids. Therefore, the solar activity research has both 
scientific and social impacts. The major database is built 
up from digitized and standardized film data obtained 
by several observatories around the world and covers a 
timespan more than 100 years. After careful calibration, 
we will develop feature extraction and data mining tools 
and provide them together with the comprehensive database for the astronomical community. Our final goal is 
to address several physical issues: filament behavior in 
solar cycles, abnormal behavior of solar cycle 24, largescale solar eruptions, and sympathetic remote brightenings. Significant progresses are expected in data mining 
algorithms and software development, which will benefit the scientific analysis and eventually advance our 
understanding of solar cycles.  
 
Keywords: solar cycle, Hα, filament, multiparameter calibration, standardization, feature extraction, solar activity pattern. 
 
 
 
 
 

 
BACKGROUND 
The Sun is the only main sequence star with periodic 
activities that dominates the Sun-Earth environment and 
has impacts on human lives. Strong solar activities such 
as CMEs can affect spacecraft systems, disrupt communications, and even damage ground-based power systems. To understand and forecast the effects of solar 
activity on Earth’s environment remains one of the main 
research problems in solar physics. However, our understanding of the fundamental physics of solar activities is 
still poor. For example, the mechanism of formation of 
solar cycle variation is unclear so far. In the literature, the 
Maunder minimum is well-known, and the abnormally 
depressed solar activity between cycles 23 and 24 has recently started to be discussed. However, their occurrences 
are big puzzles to solar physicists. On the other hand, the 
existing observational data are not well calibrated and organized to serve efficient and precise investigations.  
In particular, long-term observations are needed to 
study variation of solar activities in multiple solar cycles. Such activities include large-scale eruptions, Moreton waves, sympathetic eruptions, remote brightening 
[Tang, Moore, 1982], coronal dimming, filament oscillations, and so on. Systematic studies of these largescale events are essential to understand the topological 
magnetic structures and hence the eruptive events.  
The Moreton wave is a disturbance propagated by a 
wave and generally accompanied by a flare. It was first 
detected on Hα filtergrams by Moreton, Ramsey [1960]. 

So far, since Moreton wave events are not commonly 
observed, it is necessary to proceed to a more systematic 
analysis by taking advantage of the large Hα data sample all over the world.  
The planned database is extremely useful in investigating large-scale helicity patterns, which are believed 
to have a close relationship with solar activities. For example, it can be used to study the possible sign reversal problem in the hemisphere helicity rule. (e.g., [Bao, Ai, Zhang, 
2000; Hagino, Sakurai, 2002; Pevtsov et al, 2008]). 
It is necessary to establish a complete filament catalog in multiple solar cycles. The statistics study of filament properties in many cycles is restricted by discontinuous observations, inconsistent calibration, and incomplete samples from different instruments. It is well 
known that filaments typically fall into three categories 
[Hansen R., Hansen S., 1975] according to their latitudes. We pay attention to large-scale filaments in middle and high latitudes as they are high contrast features 
and their properties are more closely connected with 
solar cycle problems [Brajsa et al. 1990]. Pevtsov, Balasubramaniam, Rogers [2003] studied chirality of 
chromospheric filaments, using a limited data set of 
scanned images from the NSO/SP database. In this project, we further promote the research into solar helicity 
and chirality problems (e.g., [Martin, Bilimoria, Tracadas, 1994; Rust, Martin, 1994; Pevtsov, Canfield, 
Metcalf, 1995]), through a complete, unified, and calibrated digitized full-disk Hα data set.  

Формирование векового ряда данных по солнечной хромосфере…          Construction of a century solar chromosphere data…  

7 

The database covering multiple solar cycles is being 
constructed in stages. In our project, by combining the 
large-scale structure of solar eruptions, the long-term 
characteristic variation of filament statistics, and the 
abnormal behavior of solar cycles, the century-long nine 
solar cycles’ chromospheric data set around the world 
will be integrated. Upon accomplishment, it can provide 
strong support for further analysis of solar activities and 
can serve as the foundation for developing methods and 
references for deeply exploring the scientific values of 
astronomical observations. Our project can give theoretical support to the space weather forecast, which is becoming more and more urgent; and tools developed can 
be applied to the national space weather forecast system. Hence, this project has an important scientific value and social significance. 
Meanwhile, information and feature extraction is the 
basis for the long-cycle data set building process. In the 
SDO era, observation data already have the features of 
big data, namely the characteristics of mega data (volume, variety, velocity, variability, veracity, value, and 
complexity). For example, browsing images one by one 
from a data set with a second-sample rate and trying to 
find the phenomena of interest are beyond the ability of 
a personal computer system. Therefore, big data characteristics have posed a new challenge to the data analysis. 
To analyze and process SDO data, the United States, the 
European Union, and other countries have developed 
many data mining tools for all kinds of solar features and 
solar event parameters. Until now, there are seven special 
workshops held to address the key subject of solar image 
data and processing. The stability, accuracy, and efficiency 
of solar information extraction and corresponding tools are 
the main topics of these workshops. Automatic processing 
methods for massive amounts of data are also discussed in 
these workshops [Aschwanden, 2010]. 
Carried out for more than 100 years (at the Kodaikanal Observatory built in 1899, Hα observation began 
in 1912), full-disk Hα observations have provided a 
significant amount of data. However, there was no internationally accepted standard for calibration until 
Zharkova et al. [2002] published their Hα data standardization process. The authors described the correction of 
non-uniformity of the disk shape and intensity, using 
data from the Meudon Observatory in France (a member 
of the Global High Resolution Hα Network). Ermolli et 
al. [2009] studied Calcium plage areas over nine solar 
cycles, using full-disk Ca II K data from three observatories. The obvious discrepancy between the data sets 
demonstrates that a unified calibration standard for fulldisk solar images is necessary but difficult to establish. 
In terms of extraction of solar data, implementations 
have been developed in recent years based on different 
models such as Bayes classifier, wavelet transform, 
morphology [Cui Zhao et al., 2016], artificial neural 
network, support vector machine (SVM) [Qu M. et al., 
2003], deep learning [Sheng Zheng et al., 2016], and so 
on. Sometimes, better results can be obtained by combining several methods. 
In recent years, significant progress has been made 
in filament detection and statistics studies of filaments 
and their eruptions. Yuan et al. [2011] presented a 
method for filament recognition and feature extraction, 

using examples of 125 Hα images, mainly from the Big 
Bear Solar Observatory (BBSO) and other three stations 
(the samples are not large: 10 years, one image per 
month; the filament skeleton process and calculation are 
fulfilled, but the chirality calculation are not). Hao, 
Fang, Chen [2013] have studied 13832 filaments from 
3470 Hα images obtained by the Mauna Loa Observatory during the solar cycle from 1998 to 2009. Hao [PhD 
Thesis, 2015] extends the analysis to cover three solar 
cycles based on BBSO Hα data; many algorithms have 
been developed to calculate the interconnection between 
filament fragments, filament spine, tilt angle, chirality 
(not solid definition), evolution tracking, and migration 
statistics. Tlatov et al. [2016] published the results of 
filament tilt angles and distribution of filament butterfly 
diagrams for nine solar cycles. The data set of Tlatov et 
al. [2016] comprises historical synoptic charts obtained 
manually from digitized data. It should be noted that the 
manual method is still the most accurate way to draw 
such charts, but it is time consuming.  
In summary, to serve astronomy research purposes, 
we plan to integrate as many existing long-duration observations from different observatories as possible. 
Combining and calibrating historical data make up an 
important direction of solar and astronomical data processing. “Intensity calibration, geometric distortion, 
resolution rescale, large scale inhomogeneity (include 
cloud problem), etc.” can be expected to be the main 
difficulties in this project.  
In addition to the new data archive, algorithms and 
tools are being developed for information extraction, 
especially for automatic recognition of relevant astronomical events.  
At present, due to the close cooperation among 
BBSO, NSO/SP, Kodaikanal Observatory, and GHN, 
century-long Hα data sets have been collected and combined successfully. The former Soviet Union Hα data 
archives, methods and tools will be included in our future cooperation. 
 
2.  
MAIN RESEARCH CONTENTS 
The technical component of our project involves integrating multiple databases globally (primarily fulldisk Hα) and developing methods and tools for automatic feature detections. The scientific products are based 
on the technical component and address several important issues in solar physics such as the global scale 
solar eruption, remote brightening, Moreton wave, multi-cycles filament property statistics, and abnormal behavior of solar cycles 23 and 24. These contents can be 
distributed into three stages: to establish the comprehensive database and preprocess full-disk images; to develop methods and tools for filament feature recognition; to 
discover recurring patterns of solar activities. Below are 
the detailed descriptions of each task: 
Comprehensive database 
Firstly, time stamps are recovered. Before CCD 
cameras became popular in astronomical observations, 
the widely used recording medium was film or plate. 
The observing time was photographed as digital clocks 
on images. As shown in Table, the data volume is 
huge, spanning multiple solar cycles. Therefore, manually  

Ганхуа Лин

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Формирование векового ряда данных по солнечной хромосфере…  
  Construction of a century solar chromosphere data… 

9 

(Russia, NSO/USA), Prof. W.PoTzi (KOSER/Austria), 
and Prof. Dipankar (Kodaikanal Observatory/India) for 
providing their data during the data gathering. 

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Astrophys. J. 2009, vol. 698, pp. 1000–1009.  
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Martin S.F., Bilimoria R., Tracadas P.W. Magnetic field 
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whirls and filament type. Astronomical Society of the Pacific 
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Yongli Feng, Jinping Tao, Daoyuan Zhu, Li Xiong. Sunspot 
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Solar Filament Segmentation and Characterization. Solar Phys. 
2011, vol. 272, pp. 101. DOI: 10.1007/s11207-011-9798-2. 
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Как цитировать эту статью 
Ганхуа Линь, Сяо-Фань Ван, Сяо Ян, Со Лю, Мэй Чжан, 
Хайминь Ван, Чан Лю, Янь Сюй, Тлатов А.Г., Демидов М.Л., 
Боровик А.В., Головко А. Формирование векового ряда данных 
по солнечной хромосфере для исследований, связанных с солнечной 
активностью. Солнечно-земная физика. 2017. Т. 3, № 2, С. 5–9. 

How to cite this article 
Ganghua Lin, XiaoFan Wang, Xiao Yang, Suo Liu, Mei Zhang, 
Haimin Wang, Chang Liu, Yan Xu, Tlatov A.G., Demidov M.L., 
Borovik A., Golovko A. Construction of a century solar chromosphere 
data set for solar activity related research. Solar-Terrestrial Physics. 
2017. Vol. 3. Iss. 2. Р. 5–9. 

Солнечно-земная физика. 2017. Т. 3, № 2 
Solar-Terrestrial Physics. 2017. Vol. 3. Iss. 2 

10 

УДК 520.24, 681.51 
Поступила в редакцию 15.11.2016 
DOI: 10.12737/22596 
Принята к публикации 02.04.2017 

НАПРАВЛЕННАЯ ФИЛЬТРАЦИЯ ДЛЯ ОБРАБОТКИ  
ИЗОБРАЖЕНИЙ/ВИДЕОИЗОБРАЖЕНИЙ СОЛНЦА  

GUIDED FILTERING FOR SOLAR IMAGE/VIDEO PROCESSING 

Лун Cюй 
Главная лаборатория исследования солнечной активности, 
Национальные астрономические обсерватории, Китайская академия наук,  
Пекин, Китай, lxu@nao.cas.cn 
Йихуа Ян  
Главная лаборатория исследования солнечной активности, 
Национальные астрономические обсерватории, Китайская академия наук,  
Пекин, Китай 
Цзюнь Чэн  
Главная лаборатория исследования солнечной активности, 
Национальные астрономические обсерватории, Китайская академия наук,  
Пекин, Китай 

Long Xu 
Key Laboratory of Solar Activity, National Astronomical 
Observatories, Chinese Academy of Sciences,  
Beijing, China, lxu@nao.cas.cn 

Yihua Yan 
Key Laboratory of Solar Activity, National Astronomical 
Observatories, Chinese Academy of Sciences,  
Beijing, China 

Jun Cheng 

Key Laboratory of Solar Activity, National Astronomical 
Observatories, Chinese Academy of Sciences,  
Beijing, China 

Аннотация. В данной работе предлагается новый 
алгоритм повышения четкости изображений, использующий направленную фильтрацию для улучшения изображений и видеоизображений Солнца, 
который позволит легко выделять существенные 
мелкие структуры. Предлагаемый алгоритм может 
эффективно устранять шумы на изображениях, в 
том числе гауссовы и импульсные шумы. Кроме 
того, он может выделять волокнистые структуры 
на/за солнечным диском. Такие структуры наглядно 
демонстрируют развитие солнечной вспышки, протуберанца, выброса корональной массы, магнитного 
поля и т. д. Полученные экспериментальные результаты показывают, что предложенный алгоритм 
значительно 
повышает 
качество 
изображений 
Солнца по сравнению с первоначальными и несколькими классическими алгоритмами улучшения 
изображений, что облегчит определение всплесков 
солнечного радиоизлучения по изображениям/ 
видеоизображениям Солнца. 

Ключевые слова: направленный фильтр, гауссов фильтр, двусторонний фильтр, сохранение краев, 
повышение качества изображения. 

Abstract. A new image enhancement algorithm employing guided filtering is proposed in this work for 
enhancement of solar images and videos, so that users 
can easily figure out important fine structures imbedded 
in the recorded images/movies for solar observation. The 
proposed algorithm can efficiently remove image noises, 
including Gaussian and impulse noises. Meanwhile, it 
can further highlight fibrous structures on/beyond the 
solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal 
mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives 
significant enhancement of visual quality of solar images 
beyond original input and several classical image enhancement algorithms, thus facilitating easier determination of interesting solar burst activities from recorded 
images/movies. 

Keywords：guided filter, Gaussian filter, bilateral 
filter, edge preserving, image enhancement. 

INTRODUCTION 

When acquired and transmitted, images may be 
contaminated by noises. Therefore, images are usually 
denoised [Lu, Jian, et al., 2008; Sun, Xiaoli, Min Li, 
Weiqiang Zhang, 2011; Chen, Bo, et al., 2012; Han, Yu, 
et al., 2014; Wang, Jiefei, et al., 2016] before being displayed. A Gaussian filter can efficiently eliminate noises 
from images, especially addictive image noises, like 
Gaussian white noise. However, it may destroy edges of  

an image while denosing. The filter implements the image filtering task regardless of image content. Specifically, its weights for averaging nearby pixels over a pixel 
depend only on Euclidian distances of the nearby pixels 
to this central pixel. They are independent of intensities 
of pixels of an image in processing. Thus, the Gaussian 
filter would result in smoothed edges as it is across 
edges. To overcome this shortcoming of the filter, it 
should depend on the image content, i.e. the weights 

Лун Cюй, Ихуа Янь, Цзюнь Чэн 
Long Xu, Yihua Yan, Jun Cheng 

11 

should be given not only by pixel position but also by 
pixel intensities of an image. For this purpose, 
edge-preserving filters have been developed and widely 
used for image processing. It can well preserve edges of 
objects in an image while denosing it.  
A 
bilateral 
filter 
is 
the 
most 
popular 
of 
edge-preserving filters [Tomasi, Manduchi, 1998; Chen 
Xu, Min Li, Xiaoli Sun, 2013]. It is a non-linear, 
edge-preserving and noise-reducing smoothing filter for 
images. During image processing, the intensity value at 
each pixel in an image is replaced by a weighted average 
of intensity values of nearby pixels. The weights depend 
not only on the Euclidean distance of nearby pixels to the 
central pixel, but also on intensity values of nearby pixels. We can thus preserve sharp edges in an image while 
denosing it. Despite being so popular, the bilateral filter 
has a number of flaws. It may suffer from “gradient 
reversal” artifacts, as discussed in [Durand, Dorsey, 
2002; Bae, Paris, Durand, 2006]. The reason is that when 
a pixel (often on an edge) has few similar pixels around 
it, the Gaussian weighted average is unstable. In this 
case, the filter results may exhibit unwanted profiles 
around edges [He, Sun, Tang, 2013]. Another flaw of 
this filter is its high computational complexity.  
In view of the flaws of the bilateral filter, new designs 
of fast edge-preserving filters have been investigated. 
The Edge-Avoiding Wavelets (EAW) [He, Sun, Tang, 
2013] is O(n) time complexity. The filter kernel size is 
powers of two, which limits its application. Another O(n) 
time filter is known as the domain transform filter proposed by Gastal and Oliveira in [Gastal, Oliveira, 2011]. 
There are also some implicit filters in the literature, 
which are usually realized in solving optimization problems [Briggs, Henson, McCormick, 2000; Saad, 2003]. 
This process is often computationally expensive. He et 
al. have proposed a linear translation-variant filtering 
with explicit form, called guided filter in [He, Sun, Tang, 
2013]. In the guided filter, a guidance image is additionally introduced to contribute edge preserving along 
with the Gaussian filter. It can be the same as the input 
image. In this work, we firstly apply the guided filter to a 
solar image/video taken by the Atmospheric Imaging 
Assembly (AIA) aboard the Solar Dynamics Observatory 
(SDO) [http://sdo.gsfc.nasa.gov] for edge-preserving 
filtering. Then, we apply the Difference of Gaussian 
(DoG) filter to the original input image/video to extract 
details of the image/video. For the image/video enhancement purpose, the details are enlarged and then are 
combined by the filtered output of the guided filter to 
produce the final enhanced image/video. 
The rest of this paper is organized as follows. Section 
I introduces related works. Section II describes the proposed image enhancement algorithm. Section III presents 
experimental results. The final section concludes this 
paper. 

RELATED WORKS 

As shown in Figure 1, the Gaussian filter is described 
only by a spatial kernel Gs (xi–xj). Its weights are given 
by Euclidian distances of the nearby pixels to the current 

Figure 1. Flowchart of a bilateral filter 

pixel. Pixels near the current pixel make a greater contribution than those far from the current pixel. To represent the contribution of pixel intensity to image filtering, another kernel Gr(Ii–Ij), named range kernel, is defined. It is designated by an additional guided image I. 
This guided filter can be the same as the input image. The 
combination of Gs and Gr could filter an image in an 
edge-preserving manner, which results in a bilateral filter.  
To overcome the gradient reversal artifacts of the 
bilateral filter, improvements on the bilateral filter result 
in the guided filter [He, Sun, Tang, 2013]. It has not only 
a good edge-preserving property like the bilateral filter, 
but also it does not suffer from the gradient reversal 
artifacts. 
For the integrity of this paper, we outline the basic 
principle of the guided filter as follows: 
• Input: a guidance image I, a filtering input image p,
and an output image q (I and p can be identical) 
• Two assumptions:
1. A local linear model between the guidance I and
the filtering output q: 
,
ω
i
k
i
k
k
q
a I
b
i


 
. (ak, bk) are 
linear coefficients assumed to be constant in ωk.. Since 
q=aI, this linear model ensures that q has an edge 
only if I has an edge. 
2. Another linear relation between the output q and
the input p: qi=pi–ni (n represents noise). To solve (ak, 
bk), an optimization is to minimize qi–pi while maintaining the first linear model as: 

2
2

ω
(
,
)
((
)
)
k
k
k
i
k
i
k
i
k

E a b
a I
b
p
a








, 
 (1)

where  is a regularization parameter penalizing the 
large ak.  
• The solution:

ω

2

1
μ
ω

σ

i
i
k
k
i
k

k
k

I p
p

a









,  
(2) 

μ
k
k
k
k
b
p
a


,  

k and 
2
k
  are the mean and variance of I in k,  
represents the number of pixels in k
For 
,
I
p

 (2) becomes  

2

2
σ
σ

k
k
k
a 
 ,  
(3)