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.

翻译：图像与视频中的失真识别与校正是确保下游视觉应用性能的关键。本文提出一种双层顺序规划方法，用于自动化图像失真分类与校正，以替代传统基于固定试错流程的图像处理方案。上层模块负责检测输入图像中存在的失真类型（若存在失真），下层模块则从外部提供的候选算法集合中选择具体执行算法。该双层架构在推理阶段以前向传播单次执行的形式运行，并可通过迭代查询直至恢复原始图像。我们在包含丰富失真类型的COCO图像数据集上进行目标检测任务实验，结果表明本方法相较于三种基线模型均取得性能提升。本方法的优势在于其动态重构能力——根据输入图像条件进行自适应调整，且在推理时对未见过的候选算法具有泛化性，因其仅依赖于图像嵌入输出的比较结果。