  Image-level corruptions and perturbations degrade the performance of CNNs on
different downstream vision tasks. Social media filters are one of the most
common resources of various corruptions and perturbations for real-world visual
analysis applications. The negative effects of these distractive factors can be
alleviated by recovering the original images with their pure style for the
inference of the downstream vision tasks. Assuming these filters substantially
inject a piece of additional style information to the social media images, we
can formulate the problem of recovering the original versions as a reverse
style transfer problem. We introduce Contrastive Instagram Filter Removal
Network (CIFR), which enhances this idea for Instagram filter removal by
employing a novel multi-layer patch-wise contrastive style learning mechanism.
Experiments show our proposed strategy produces better qualitative and
quantitative results than the previous studies. Moreover, we present the
results of our additional experiments for proposed architecture within
different settings. Finally, we present the inference outputs and quantitative
comparison of filtered and recovered images on localization and segmentation
tasks to encourage the main motivation for this problem.
