# DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions

arXiv: https://arxiv.org/abs/2407.16302 · HTML: https://mlsanity.com/paper/2407.16302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms](https://mlsanity.com/paper/1605.07116/) (2016)
- [MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings](https://mlsanity.com/paper/2102.12461/) (2021)
- [Image Distortion Detection using Convolutional Neural Network](https://mlsanity.com/paper/1805.10881/) (2018)
- [Automatic Algorithm Selection In Multi-agent Pathfinding](https://mlsanity.com/paper/1906.03992/) (2019)
- [Disentangling Image Distortions in Deep Feature Space](https://mlsanity.com/paper/2002.11409/) (2020)
- [Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception](https://mlsanity.com/paper/2209.02991/) (2022)

## Abstract (arXiv)

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.