First light for my new lens

1 stack of 19 images, Canon 800D at ISO 800, Takumar 135mm f2.5 lens at f4, 30s exposures, Omegon Lx2 tracking mount. 45 darks, 120 biases. Processed in PixInsight as below

***** Integration

lightvortexastronomy tutorial (www.lightvortexastronomy.com/tutorial-pre-processing-cali...)

* CC defect list + master dark

* weighing: (15*(1-(FWHM-FWHMMin)/(FWHMMax-FWHMMin)) + 15*(1-(Eccentricity-EccentricityMin)/(EccentricityMax-EccentricityMin)) + 20*(SNRWeight-SNRWeightMin)/(SNRWeightMax-SNRWeightMin))+50

* img 1135 reference

* star align - distortion relaxed to 0.3

* integration - winsorized sigma clipping

***** Linear processing

*** Crop

*** DBE, 0.5 tolerance

*** Color calibration

* SNCR 0.5

* Background neutralization (using small area under middle star as sample background - hard to find true neutrality in Orion flickr.com/photos/deepskycolors/16774216742/in/faves-1833...)

* Color calibration w/background and structure detection

*** Deconvolution

* Created star_mask_large - large scale structure 2, small scale 1, noise threshold 0.1, scale 8

* Created range mask - extracted luma, applied standard STF, then histogram shadow = 0.25 mids = 0.3 high = 1

* Deconvolve with range mask on, 100 interations, custom PSF, dark 0.01 bright 0.004, local deringing with star mask, wavelet regularization

*** Star reduction

Small star mask - noise 0.15, scale 4, small scale 3 comp 1, smoothness 8, binarize, midtones = 0.02

Range mask from that, 0.05-1

Apply, erosion operator 2 iterations 0.6 with shape oval

*** Linear noise reduction

jonrista.com/the-astrophotographers-guide/pixinsights/eff...

*TGV - small noise

Created TGV masks - extracted luminosity, standard stretch (luminance_mask), curved it with black point at ~0.2 and white at ~0.5, moved histogram point to middle (tgv_mask)

apply tgv mask inverted to the image, give luma mask as local support

TGV chroma str 7 edge protection 2E-4 smoothness 2 iterations 500

TGV luma str 5 edge protection 1E-5 smoothness 2 iterations 500

*MMT - larger noise and TGV artifacts

Created MMT mask - extract luminosity, standard stretch, move histogram point to 75%, apply low range -0.5. Apply inverted

MMT mask - 8 layers, threshold 10 10 7 5 5 2.5 2 2 on rgb

*****Nonlinear

***Initial stretch

*Autostretch, apply to hist

*Create full star mask, max(star_mask_large, star_mask_small)

* HDR transform, 8 layers, B3 spline, star mask applied inverted, preserve hue, lightness mask

***MLT stretch

www.stelleelettroniche.it/en/2014/09/astrophoto/m42-ngc19...

**Initial

* created a new multiscale linear transform, kept 4 layers using linear interpolation

* diffed from original image to create a "blurred" version of original image

* extracted luminance from original, used as mask on blurred version

* used curves to create s shape in luminance, inflection 3/4 up, and pump up saturation a lot

* pixelmath sum the 3, rescaled, back to original image

**Second

* new multiscale linear transform, keep 5 layers

* diff from original

* extract luminance from blurred image, to use as a mask

* masked blurred image with its own luminance, gave it s-shaped RGB curve, big boost in saturation

* pixelmath sum the 3, rescaled, back to original image

***Star and nebula mask:

Range select to catch nebulas, blur with 3 a troux wavelet iterations (removing first 5 layers), add to star mask to obtain star and nebula mask

***Local histogram equalization

kernel 32 contrast 1.5 amount 0.35, with star and nebula mask on

***Nonlinear NR

*Luminance mask, clipped shadows

*ACDNR stdev 4 chroma 2 luma, amount 0.9 chroma 0.7 luma

***Dark structure enhance

* 5x5, 3 iterations, amount 0.25

*** Sharpen

* Sharpen with multiscale linear transform, bias layers 2-6 (0.05, 0.05, 0.025, 0.012, 0.006)