# DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions Source: [DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:44:58.067Z Authors: Aditya Kapoor, Harshad Khadilkar, Jayvardhana Gubbi Published: 2024-07-23T08:57:11.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()