Editor’s Note: For the remainder of 2025, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded articles published in AAS journals this year. The usual posting schedule will resume January 2nd.
ImageMM: Joint Multi-Frame Image Restoration and Super-Resolution
Published September 2025
Main takeaway:
A team led by Yashil Sukurdeep (Johns Hopkins University) developed a new method for processing and combining ground-based astronomical images. The authors’ algorithm, called ImageMM because it leverages a method called majorization–minimization, yielded greater detail in bright sources and distinguished more faint sources from the background than existing methods.
Why it’s interesting:
As ground-based telescopes grow larger and their cameras grow more sensitive, they must still contend with one unfortunate fact: they are trapped beneath Earth’s atmosphere, which blurs fine details in astronomical images. To extract as much information as possible from beneath Earth’s blurry, ever-shifting atmosphere, researchers have developed numerous strategies for cleaning, combining, and sharpening astronomical images. From simple averaging of multiple exposures to complex statistical techniques, each of these methods has unique strengths and weaknesses. ImageMM succeeds in quieting background noise and sharpening images of both extended and point-like sources while avoiding many of the pitfalls of statistical methods.
How they tested the new method:
An example of satellite-trail removal by ImageMM. Click to enlarge. [Adapted from Sukurdeep et al. 2025]
Citation
Yashil Sukurdeep et al 2025 AJ 170 233. doi:10.3847/1538-3881/adfb72