  Camera anomalies like rain or dust can severelydegrade image quality and its
related tasks, such as localizationand segmentation. In this work we address
this importantissue by implementing a pre-processing step that can
effectivelymitigate such artifacts in a real-time fashion, thus supportingthe
deployment of autonomous systems with limited computecapabilities. We propose a
shallow generator with aggregation,trained in an adversarial setting to solve
the ill-posed problemof reconstructing the occluded regions. We add an enhancer
tofurther preserve high-frequency details and image colorization.We also
produce one of the largest publicly available datasets1to train our
architecture and use realistic synthetic raindrops toobtain an improved
initialization of the model. We benchmarkour framework on existing datasets and
on our own imagesobtaining state-of-the-art results while enabling real-time
per-formance, with up to 40x faster inference time than existingapproaches.
