When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study
This work addresses a gap for computer vision researchers by providing a comparative study that could enhance applications like surveillance, but it is incremental as it focuses on benchmarking rather than proposing new methods.
The paper tackles the lack of integrated evaluation between super-resolution (SR) and camouflaged object detection (COD) by benchmarking SR methods on COD datasets and assessing COD models on SR-processed data, aiming to bridge these domains and uncover new experimental insights.
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.