Solar Irradiance, Image Restoration and Structure Identification - - PowerPoint PPT Presentation

▶
solar irradiance image restoration and structure
SMART_READER_LITE
LIVE PREVIEW

Solar Irradiance, Image Restoration and Structure Identification - - PowerPoint PPT Presentation

Nov 07, 2023 242 likes •589 views

LASP REU 2007 Solar Irradiance, Image Restoration and Structure Identification Ryan Schilt Mark Rast Serena Criscuoli Juan Fontenla & Nathan Goldbaum 1 Introduction Solar Irradiance Average incoming solar radiation How can

slide-1
SLIDE 1

LASP REU 2007

Solar Irradiance, Image Restoration and Structure Identification

Ryan Schilt Mark Rast Serena Criscuoli Juan Fontenla & Nathan Goldbaum

1

slide-2
SLIDE 2

Introduction

Solar Irradiance

Average incoming solar radiation

How can this be modeled?

Varying Magnetic Features on the surface of the Sun will

change how much radiation is observed

The 'quality' of an image could also change how solar

features are quantified.

In order to develop a rich model for solar irradiance, it is

necessary to understand how solar images can be corrected fo defects and how that correction will affect the way the solar features are identified.

2

slide-3
SLIDE 3

Introduction:Scope of Presentation

PSPT

Specifications Image Gathering

Magnetic Feature Identification

Identifying different features on surface Use total feature area to better quantify irradiance

Image Defects and Restoration Image Control Restoration and Identification Results

3

slide-4
SLIDE 4

Precision Solar Photometric Telescope (PSPT)

Hawaii, Southeast of Honolulu Mauna Loa Solar Observatory (MLSO)

http://www.mlo.noaa.gov/livecam/livecam.html

PSPT

4

slide-5
SLIDE 5

Precision Solar Photometric Telescope (PSPT)

15 cm Refracting Observed Wavelength

CaIIK (393.4nm) blue continuum

(409.4nm)

red continuum

(607.1nm)

1TB of data per year

~2.7 GB a day

5

slide-6
SLIDE 6

Precision Solar Photometric Telescope (PSPT) Image Gathering

6

slide-7
SLIDE 7

Precision Solar Photometric Telescope (PSPT)

Blue Continuum Red Continuum

images: http://rise.hao.ucar.edu/links/mlso_hourly_images.html

CaIIK

7

slide-8
SLIDE 8

Solar Feature Extraction: What features are there?

Active Network Average Network Sunspot Umbra & Penumbra Plage (Fac Average Superganu 8

slide-9
SLIDE 9

Solar Feature Extraction: Annuli and Averages

9

i Heliocentr

  • f

Cosine radius solar radius annulus 1

2

= = =

  • =

μ μ

s a s a

r r r r

slide-10
SLIDE 10

Solar Feature Extraction: Calibrated Models

10

slide-11
SLIDE 11

Solar Feature Extraction: Original vs Identified

11

Sunspot Penumbra Sunspot Umbra Faculae Plage Active Network Average Network Average Supergranule

slide-12
SLIDE 12

Image Defects and Restoration

Instrument Effects

Quadrant Defects

Artifact of how images are gathered (old CCD)

Flat Field Defects

Artifacts left in during the flat field process

Natural Effects

Solar Limb-Darkening

Result of increased optical depth of cooler atmosphere

Stray Light

Scattering and blurring by the Earth's atmosphere and the

PSPT

Solar features (sunspots, faculae, etc.) are degraded

12

slide-13
SLIDE 13

Image Defects and Restoration: Quadrant and Flat- Field Defects

**Histogram equalization: intensity in the image is propo to the number of pixels in a given original intensity 13

slide-14
SLIDE 14

Image Defects and Restoration: Center to Limb Variation

Blue image processed 2005/07/02 17:02 UT Blue image contrast 2005/07/02 17:02 U 14

slide-15
SLIDE 15

Image Defects and Restoration: Main Defect Cause

Turbulence in Earth's atmosphere bends wavefront

Scintillation Agitation Smearing

Scintillation: Not Addressed in restoration Agitation Smearing 15

slide-16
SLIDE 16

Image Defects and Restoration: How is distortion

  • bserved?

16

slide-17
SLIDE 17

Image Defects and Restoration: Correction Procedure

  • Unaltered image: Image if viewed without any distortion.
  • PSF: Describes both Atmospheric distortions.
  • Noise: Noise due to unpredictable actions.

Observed Image Unaltered Image Point Spread Function(PSF) Noise 17

) , ( ) , ( ) , ( ) , ( y x n y x s y x i y x i +

  • =
slide-18
SLIDE 18

Image Defects and Restoration: Correction Procedure

  • Unaltered Image estimated through Inverse Fourier

Transform.

  • Noise is generated randomly
  • The real aim of the procedure is to properly describe the

PSF.

Observed Image Unaltered Image Point Spread Function(PSF) Noise 18

) , ( ) , ( ) , ( ) , ( y x n y x s y x i y x i +

  • =
slide-19
SLIDE 19

After several assumptions about the distribution of the image distortions:

Image Defects and Restoration: Correction Procedure

) , ( ) , ( ) , ( ) , ( y x n y x s y x i y x i +

  • =

Observed Image Unaltered Image Point Spread Function(PSF) Noise 19

3 4 3 2 3 3 2 2 1 1 2 4 2 1 ) / ( 4 ) / ( 3 ) / ( 2 1

); 1 ( ); 1 )( 1 ( ); 1 ( ) ( } { ) (

2 3 2 2 2 1

a C a a C a a C a C b r A a e C e C e C C r s

b r b r b r

=

  • =
  • =
  • =

+ + + + =

slide-20
SLIDE 20

Restored Image: 20070405.1740

Image Defects and Restoration: Original vs Restored

20 Original Image: 20070405.1740

slide-21
SLIDE 21

Image Defects and Restoration: Poor Restoration

21 Poor Restoration: 20070317.1730

slide-22
SLIDE 22

Image Control Preparation

Control requirements

High “quality” images Large data set Detailed images

Selection process

Choose observation days that have many images Find quality data and create histogram to divide image

into three groups; good, bad and ugly. To test restoration and identification methods you must have a control!

22

slide-23
SLIDE 23

Inflection Point

Image Control Preparation: Defining Quality

23

slide-24
SLIDE 24

Control Image Preparation: Defining Quality

Inflection Point Width at Half Max Reflected Line

Quality is average of width for each limb The sharper the image, the narrower the width

24

slide-25
SLIDE 25

Image Control Preparation: Comparison of Qualities

25

slide-26
SLIDE 26

Image Control Preparation: Comparison of Qualities

Good Bad Ugly 26

slide-27
SLIDE 27

Restoration and Extraction Results: Area over a Day

27

slide-28
SLIDE 28

Restoration and Extraction Results: Area over a Day

28

slide-29
SLIDE 29

Restoration and Extraction Results: Area over a Day

29

slide-30
SLIDE 30

Restoration and Extraction Results: Good and Bad

30

slide-31
SLIDE 31

Summary and Conclusions

31

Quality of an image decreases over an observation day

Sun heats the atmosphere creating turbulence

Active and average network increases with the restoration of a

image

Superganulation decreases with restoration of image Change in area between restored and non-restored images is s

large that restoration gives an images that has better quality th the highest quality image gathered for that day

slide-32
SLIDE 32

Future Plans

32

Perform restoration on all images over a single observation day.

how identified areas change. If restored correctly, images would same identified features

Compare the restoration of the worst quality image in a single

with the best unrestored image of that day

Structure Identification

Structure models are normalized to annulus mean. Restoration

changes the distribution of intensity, leaving the mean modera

  • unchanged. An improvement would normalize to something t

is not constant with restoration

Image Restoration

Improve restoration algorithm to not over restore an image Restoring no further than the best quality images of that day o

highest quality images of the PSPT

Prevents restoration beyond the quality the PSPT will allow

slide-33
SLIDE 33

References

  • Serena Criscuoli, “Phase Diversity,” INAF Astronomical Observatory of Rome
  • J.W. Brault & O.R. White, “The Analysis and Restoration of Astronomical Data via the

Fast Fourier Transform,” NASA Astrophysics Data System, no. 13 (1971) 169-189.

  • J. Fontenla & G. Harder, “Physical modeling of spectral irradiance variations,” Societá

Astronomica Italiana, no. 76 (2005) 826

  • Juan Fontenla, Oran R. White, Peter A. Fox, Eugene H. Avertt and Robert L. Kurucz,

“Calculation of Solar Irradiances. I. Synthesis of the Solar Spectrum,” The Astrophysica Journal, no.518 (1999) 480-499

  • Mark Rast, psptdescription.doc, June 10, 2007
  • Mark Rast, Precision Solar Photometric Telescope, http://lasp.colorado.edu/pspt_access/
  • Mark Rast, Radiative Inputs of the Sun to Earth: Precision Solar Photometric Telescope

http://rise.hao.ucar.edu/ 33