IRSTDID-800: A Benchmark Analysis of Infrared Small Target Detection-Oriented Image Destriping

Deep learning-based single-image infrared (IR) destriping has made significant advances. However, these methods are typically evaluated using “synthetic” images with specific stripe noise, making it unclear how well they handle “real” IR images. In fact, a clear and fair benchmarking of the existing destriping methods on real images, especially for the downstream IR small target detection (IRSTD) task, is currently an open gap. To tackle this problem, we introduce a novel benchmark, called IRSTD-oriented image destriping (IRSTDID-800), which thoroughly showcases the real distribution of IR small targets under stripe noise perturbation for the first time. Concretely, it consists of two subsets. (1) IRSTDID-SKY: composed of 500 real-world images afflicted with stripe noise, including unmanned aerial vehicles (UAVs) of various shapes, sizes, and contrasts. Moreover, these images are annotated with precise pixel levels for objective evaluation of IRSTD. (2) IRSTDID-GND: comprising 300 real-world images featuring common daily life objects, providing a richer scene under stripe noise. Based on the proposed IRSTDID-800, we comprehensively assess the performance of ten state-of-the-art (SOTA) destriping methods across eleven metrics, including full-reference, no-reference, and task-driven metrics with six advanced IRSTD methods. Furthermore, inspired by the correlation between image quality assessment and IRSTD, we proposed a task-oriented destriping optimization strategy. A loss function is introduced for IR image destriping, leveraging the structural properties of noise as a penalty term to strengthen image destriping and IRSTD. Overall, our analysis reveals interesting observations to guide future research in destriping and IRSTD tasks. Our dataset is available at https://github.com/xdFai/IRSTDID-800.