Processing an ultra deep 393h collaboration of M81/M82

Image:

Gigapan link here: https://www.gigapan.com/gigapans/232382

I highly recommend zooming in on all of the smaller background galaxies!

Image with HII

This image has all HII data from the VLA added in blue-green, which highlights the extent of gravitational interactions.

Gigapan link here: https://www.gigapan.com/gigapans/232383

You can view all the details and interesting features of the image here: https://www.astrobin.com/tb0sou/

Processing

The processing steps here are a result of an iterative processes where I determined the best methods for this dataset. I processed the entire dataset a total of 12 times, and this was the best result.

Background Flattening

Multi-scale Gradient Removal was used to remove any widefield gradients from L, R, G, B, and Ha. Because there were stacking artifacts in the L image, MSGR was preformed twice at different scales (256 and 32) to target the different gradients.

H-alpha Continuum Subtraction

To isolate the pure H-alpha components of the image, an HRR image was created. This was then color-calibrated so that the broadband components were pure white, and then denoised and deconvoluted, and starnet was applied to regions with high amounts of smooth nebulosity. Finally, the H-alpha component was isolated from this image using the pixelmath: $T[0[ – ($T[1]-med($T[1]))

Linear RGB Processing

Because the RGB image is only used for color information, there was a strong bias towards reducing chrominance noise. The following steps were used to processes the RGB image in the linear state:

• TGV denoise targeting Chrominance
• DeepSNR with a star mask
• NoiseX with a star X
• Spectral photometric color calibration fit to a S0 type galaxy
• BlurX only targeting stars to shrink the stars

Linear Luminance processing

I worked quite hard on Luminance to balance the amount of noise reduction and detail that I could bring out. The following steps were used to processes the Luminance image in the linear state:

• Deconvolution on RGB image using the Regularized Van-Cittart algorithm
• BlurX only targeting stars to shrink the stars
• TGV denoise targeting luminance
• NoiseX with a star mask

Non-linear LRGB processing

The first steps to do when processing an LRGB image is to stretch and combine the L and RGB images. It’s very important that neither image be stretched too strongly, and that the stretch of L matches the stretch of RGB. This is the result of the initial LRGB stretch and combination:

Next, the goal is to show the intensity of the IFN without blowing out the galaxies. This is done through a number of steps. First, the image is overstretched using HT. Duplicates are pulled off, and decreasing layers of MMT are run (from layers 7 to 5) and used as a mask for HT transformation. Then the background is replaced to retain local contrast. The result is an image that has a high dynamic range but has preserved local contrast in bright regions.

Next, in order to increase contrast in the core of the galaxies, HDRMT was applied with a luminance mask, then LHE was applied with scale 256, and then the image was brightened using curves. The result is as follows:

Next, the colors of the image were targeted. A saturation mask was created using L*~SV, and then curves were used to recursively increase the saturation. Then, a Luminance MMT mask of layers 7 was used to adjhust the colors of the galaxies. Finally, the GAME script was used to adjust the colors of M81’s core.

Finally, the H-alpha image was non-linearly added using the linearization technique. GAME masks were used to add differing amounts of H-alpha to M81 and M82.