RestoreDet：用于低分辨率图像目标检测的退化等变表示
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images
摘要：
超分辨率等图像复原算法是退化图像目标检测中不可缺少的预处理模块。然而，这些算法中的大多数假设退化是固定的并且先验已知。当真实退化未知或与假设不同时，预处理模块和随后的高级任务（诸如对象检测）都将失败。在这里，我们提出了一个新的框架，RestoreDet，检测对象在退化的低分辨率图像。RestoreDet利用下采样退化作为自监督信号的一种变换，以探索针对各种分辨率和其他退化条件的等变表示。具体来说，我们学习这个内在的视觉结构的编码和解码的退化变换从一对原始的和随机退化的图像。该框架可以进一步利用具有任意分辨率恢复解码器的高级SR架构来从退化的输入图像重建原始对应。表示学习和对象检测都以端到端的训练方式联合优化。RestoreDet是一个通用的框架，可以在任何主流的对象检测架构上实现。大量的实验表明，我们的框架的基础上CenterNet取得了优越的性能与现有的方法相比，面对变异退化的情况。我们的代码很快就会发布。
英文摘要：
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in degraded images. However, most of these algorithms assume the degradation is fixed and known a priori. When the real degradation is unknown or differs from assumption, both the pre-processing module and the consequent high-level task such as object detection would fail. Here, we propose a novel framework, RestoreDet, to detect objects in degraded low resolution images. RestoreDet utilizes the downsampling degradation as a kind of transformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions. Specifically, we learn this intrinsic visual structure by encoding and decoding the degradation transformation from a pair of original and randomly degraded images. The framework could further take the advantage of advanced SR architectures with an arbitrary resolution restoring decoder to reconstruct the original correspondence from the degraded input image. Both the representation learning and object detection are optimized jointly in an end-to-end training fashion. RestoreDet is a generic framework that could be implemented on any mainstream object detection architectures. The extensive experiment shows that our framework based on CenterNet has achieved superior performance compared with existing methods when facing variant degradation situations. Our code would be released soon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
