Astrophotography: Retinex, Deconvolution & Super‑Resolution

Astrophotography blends creativity with technical skill. When you aim your camera at the night sky, your equipment faces real challenges: bright stars next to faint nebulae, lens and atmospheric blurring, and noise from long exposures.

Luckily, years of imaging research have given us tools to turn faint light into clear pictures. This article is a step-by-step MATLAB workflow for photographing the Orion Nebula.

Why Are Astro Images Hard?

A useful starting point is the standard linear degradation model of an observed frame (X):

[

X(x,y) = Y(x,y) * H(x,y) + N(x,y),

]

Here, (Y) stands for the real sky scene, (H) is the point-spread function (PSF) from your optics, the atmosphere, and tracking, (N) is the noise, and (*) means convolution. This model is the basis for most image restoration methods, including deconvolution and super-resolution. In practice, noise is often treated as Gaussian, and the PSF is usually unknown and changes over time, so blind and probabilistic methods are often used.

One effective approach is multichannel reconstruction, where you treat a series of short exposures or different bands as separate channels. Using a Bayesian…