  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.
