JWST & Hubble Data Fusion using a deep learning framework. NGC7496.

Here's another attempt at data fusion between JWST and Hubble.

I feel like there is no perfect way to do it, how to represent in visible colors something that is not visible ?

 

I tried a deep learning framework from Hui Li et al. 2018 : « Infrared and Visible Image Fusion using a Deep Learning Framework ». I found it very conservative when it comes to data. NIRcam, MIRI and HST are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. It assembles the details without one dataset overwriting another.

 

Processing sequence JWST NIRcam

Linear Pattern Substraction, Star alignement, RGB: f360m Maroon, f355m Red, f300m Green, f200w Blue. DBE, Mure Denoise, Deconvolution, TGV, masked stretch, ACDNR, Curves.

 

Processing sequence JWST MIRI

Linear Pattern Substraction, Dynamic alignment, Superstack of all filters using the Robust Chauvenet Rejection method. DBE, Masked Stretch, local histogram, Curves

 

Processing sequence of Hubble

Star Alignment, Mure Denoise, RGB as: NIRcam superstarck for maroon, f814w Red, f555w Green, f438w Cyan, f336w Blue. DBE, Deconvolution, ColorCal, TGV, Masked Stretch, ACDNR, Local Hist, Photoshop for cosmetic corrections, filling the gap with noise.

 

Deep Learning data fusion

Fusion of MIRI and NIRcam superstacks. Fusion of JWST & HST superstacks.

LRGB with the fusion as Luminance and Hubble as RGB.

 

Separate images of NIRcam and MIRI on my astrobin:

www.astrobin.com/l083yz/

Done