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NGC 7822 in the Cepheus, 



    
        

            Satwant Kumar
Powered byPixInsight

NGC 7822 in the Cepheus

Acquisition type: Electronically-Assisted Astronomy (EAA, e.g. based on a live video feed)
Getting plate-solving status, please wait...
NGC 7822 in the Cepheus, 



    
        

            Satwant Kumar
Powered byPixInsight

NGC 7822 in the Cepheus

Acquisition type: Electronically-Assisted Astronomy (EAA, e.g. based on a live video feed)

Acquisition details

Dates:
Oct. 28, 2020
Frames:
150×600(25h) (gain: 139.00) -30°C bin 1×1
Integration:
25h
Darks:
30
Flats:
30
Bias:
30
Avg. Moon age:
11.97 days
Avg. Moon phase:
91.43%
Bortle Dark-Sky Scale:
2.00
Temperature:
-17.00

RA center: 00h03m16s.46

DEC center: +67°0814.8

Pixel scale: 2.117 arcsec/pixel

Orientation: -152.273 degrees

Field radius: 2.478 degrees

More info:Open 

Resolution: 6773x5021

File size: 22.9 MB

Data source: Traveller

Description

NGC 7822 in the Cepheus.

I imaged this target during a road trip across Colorado, New Mexico, and Oklahoma.

I got lucky on a few nights with clouds after a snow storm.

Acquisition details:

H-alpha - 600s x 77 = 46,200s

OIII - 600s x 21 = 12,600s

SII - 600s x 52 = 31,200s

Total = 25 hours

Here are my processing details:

calibration, registration, and data normalization in APP

Integration with outlier rejection in APP

Local normalization correction: 4th-degree LNC with 3 iterations.

Multi-band blending (MBB) of images at 10%.

Image upscaled to 1.5x using the Drizzle algorithm. Parameters= topHatKernel with a droplet size of 0.75.

All channels/filters were integrated separately. All sessions were integrated into one session.

Integration weights were selected based on the noise in the frames.

The background was calibrated in APP using the light pollution removal tool for each filter/channel, which was followed by the histogram stretching.

Next, stars were separated from each channel using Tensorflow-2 based implementation of the StarNet tool by the original author (https://github.com/nekitmm/starnet).

Stars were extracted per filter by subtracting the extracted backgrounds from the original images. Later, these star images were combined as RGB channels to produce colored stars.

Each grayscale background image was passed through the Topaz Denoise at 15-20% noise reduction.

Next, grayscale images were weighted and combined to generate an RGB image in MATLAB using the following equations: % perform pixel math on the individual channels.

s_b = immultiply(s_b,3);% s_r = Ha, s_b=OIII, and s_g=SII

s_r = immultiply(s_r,0.9);

s_g = immultiply(s_g,1.2);

c_r = imlincomb(0.60,s_r,0.30,s_g,0.10,s_b,'uint8');

c_g = imlincomb(0.30,s_r,0.60,s_g,0.10,s_b,'uint8');

c_b = imlincomb(0.30,s_r,0.0,s_g,0.60,s_b,'uint8');

rgbImage = cat(3, 1.3*c_r, 1*c_g, 1.2*c_b);

A morphological opening was used to remove the pixelated noise in the RGB image. This opening operation removes small objects that cannot completely contain the structuring element. A disk-shaped structuring element with a radius of 2 pixels was defined to remove isolated pixels using the following commands in MATLAB.

se1 = strel('disk',2);

rgbImage_b_rm = imopen(rgbImage_b,se1);

Finally, RGB image was linearly combined with the color stars in MATLAB using following equation:

rgbImage_with_stars = imlincomb(1,rgbImage, 1, 1*stars);

Finally, Photoshop was used to adjust saturation/ hue, contrast, and brightness.

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NGC 7822 in the Cepheus, 



    
        

            Satwant Kumar