  Current trends in scientific imaging are challenged by the emerging need of
integrating sophisticated machine learning with Big Data analytics platforms.
This work proposes an in-memory distributed learning architecture for enabling
sophisticated learning and optimization techniques on scientific imaging
problems, which are characterized by the combination of variant information
from different origins. We apply the resulting, Spark-compliant, architecture
on two emerging use cases from the scientific imaging domain, namely: (a) the
space variant deconvolution of galaxy imaging surveys (astrophysics), (b) the
super-resolution based on coupled dictionary training (remote sensing). We
conduct evaluation studies considering relevant datasets, and the results
report at least 60\% improvement in time response against the conventional
computing solutions. Ultimately, the offered discussion provides useful
practical insights on the impact of key Spark tuning parameters on the speedup
achieved, and the memory/disk footprint.
