Super-resolution in astronomical images: enhancing object detection Irene Pintos Castro USC / CEFCA ipintoscastro@gmail.com Manuel Fern´andez Delgado USC (Supervisor) Andr´es del Pino CEFCA (Supervisor) Abstract—Using paired images of JAST/T80 Telescope in OAJ and of Hubble Space Telescope we aim at increasing the resolution of JAST/T80 images with the objective of enhancing object detection. Higher resolution versions of JAST/T80 images in crowded areas of the sky will significantly increase the number of stars and/or galaxies detected, enabling a better measurement of their photometry. We pre-processed both low-resolution and high-resolution images to build a dataset of paired crops, which is used to train the Real-ESRGAN neural network. We are able to get super-resolved images with a 4× scale and a FSIM=0.8903, showing an improvement in the source detection in crowded areas. However, visual inspection of bright stars and nearby galaxies reveals that there is room for learning a better model that can reproduce the PSF and the details of the extended objects. I. INTRODUCTION Telescopes are the instruments we use to observe our Universe. The cameras installed on them provide us with the data needed for its study: images and spectra. Astronomical observations from Earth are always limited by the presence of the atmosphere. While space telescopes avoid atmospheric aberrations, they are an expensive solution and will still have technical limitations, as the point spread function (PSF) consequence of the finite aperture and light dispersion within the telescope. An image shows the brightness of an object in the spatial domain, i.e., how many photons are coming from a specific location in space. Three properties of an image – size, bright- ness, and resolution – are the most important from a scientific point of view. From size, we learn about astronomical scales. From brightness, we learn the amount of energy that an object is producing, in order to investigate how it is producing that energy. The ability to distinguish one location from a nearby location is called spatial resolution. Higher resolution lets us know things like whether or not two galaxies are merging or if there are two stars close to each other instead of one star by itself. Astronomers aim at having the largest possible images with the highest possible resolution. In addition to their large size, astronomical images have special characteristics: they are commonly stored in Flexible Image Transport System (FITS) digital file format; they have high range of grey values (32-bit float); and they are usually quite noisy. Though images are grey-scale, they contain some colour information because they are taken through a colour filter. Telescopes, instruments and detectors have different sensitivities to different wavelengths and usually work with a well-defined set of filters. These filters can have a variety of widths, from broad-band to narrow-band (∼1000 ˚A to few 10 ˚A). Finally, one particularly important aspect of astronomi- cal image processing is the choice of the stretch function. The representation of the pixel values that one selects has a high impact on the number and shape of the objects that we see in the image. For instance, a logarithmic representation tends to suppress the bright parts of the image and to enhance the fainter ones. This can be desirable though it will reduce the contrast, producing a lower dynamic range. Super-resolution describes a class of methods that can upscale video or images from lower resolutions to higher ones. Such methods have been successfully demonstrated on as- tronomical imaging (e.g. [1]–[4]). Traditionally, interpolation methods such as bilinear and nearest neighbour interpolation are used for upscaling. However, these methods often intro- duce side effects such as noise amplification and blurring. If the PSF can be well determined, deconvolution techniques can be applied to removing the effect of the instrumentation and