  Distortion identification and rectification in images and videos is vital for
achieving good performance in downstream vision applications. Instead of
relying on fixed trial-and-error based image processing pipelines, we propose a
two-level sequential planning approach for automated image distortion
classification and rectification. At the higher level it detects the class of
corruptions present in the input image, if any. The lower level selects a
specific algorithm to be applied, from a set of externally provided candidate
algorithms. The entire two-level setup runs in the form of a single forward
pass during inference and it is to be queried iteratively until the retrieval
of the original image. We demonstrate improvements compared to three baselines
on the object detection task on COCO image dataset with rich set of
distortions. The advantage of our approach is its dynamic reconfiguration,
conditioned on the input image and generalisability to unseen candidate
algorithms at inference time, since it relies only on the comparison of their
output of the image embeddings.
