Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot topics in computer vision with various joint applications. For instance, low-resolution surveillance images can be successively processed by super-resolution techniques and camouflaged object detection. However, in previous work, these two areas are always studied in isolation. In this paper, we, for the first time, conduct an integrated comparative evaluation for both. Specifically, we benchmark different super-resolution methods on commonly used COD datasets, and meanwhile, we evaluate the robustness of different COD models by using COD data processed by SR methods. Our goal is to bridge these two domains, discover novel experimental phenomena, summarize new experim.

翻译：超分辨率（SR）与伪装目标检测（COD）是计算机视觉领域的两大热点，具有多种联合应用场景。例如，低分辨率监控图像可通过超分辨率技术及伪装目标检测进行连续处理。然而，以往的研究中，这两个领域总是被孤立地探讨。本文首次对两者进行了综合性的比较评估。具体而言，我们在常用的COD数据集上对不同超分辨率方法进行了基准测试，同时通过使用经超分辨率方法处理的COD数据，评估了不同COD模型的鲁棒性。我们的目标是桥接这两个领域，发现新的实验现象，并总结新的实验规律。