When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study

TL;DR

This paper conducts the first integrated comparison of Super-Resolution and Camouflaged Object Detection, benchmarking SR methods on COD datasets and evaluating COD models' robustness with SR-processed data to uncover interactions and effects.

Contribution

It provides a comprehensive comparative evaluation bridging SR and COD, highlighting their interactions and effects for the first time.

Findings

SR methods impact COD detection performance

Certain SR techniques improve COD robustness

New experimental phenomena observed in combined application

Abstract

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.