  The Transformer architecture has revolutionized the field of deep learning
over the past several years in diverse areas, including natural language
processing, code generation, image recognition, time series forecasting, etc.
We propose to apply Zamir et al.'s efficient transformer to perform
deconvolution and denoising to enhance astronomical images. We conducted
experiments using pairs of high-quality images and their degraded versions, and
our deep learning model demonstrates exceptional restoration of photometric,
structural, and morphological information. When compared with the ground-truth
JWST images, the enhanced versions of our HST-quality images reduce the scatter
of isophotal photometry, Sersic index, and half-light radius by factors of 4.4,
3.6, and 4.7, respectively, with Pearson correlation coefficients approaching
unity. The performance is observed to degrade when input images exhibit
correlated noise, point-like sources, and artifacts. We anticipate that this
deep learning model will prove valuable for a number of scientific
applications, including precision photometry, morphological analysis, and shear
calibration.
