  Deep learning (DL) networks have achieved remarkable performance in infrared
small target detection (ISTD). However, these structures exhibit a deficiency
in interpretability and are widely regarded as black boxes, as they disregard
domain knowledge in ISTD. To alleviate this issue, this work proposes an
interpretable deep network for detecting infrared dim targets, dubbed RPCANet.
Specifically, our approach formulates the ISTD task as sparse target
extraction, low-rank background estimation, and image reconstruction in a
relaxed Robust Principle Component Analysis (RPCA) model. By unfolding the
iterative optimization updating steps into a deep-learning framework,
time-consuming and complex matrix calculations are replaced by theory-guided
neural networks. RPCANet detects targets with clear interpretability and
preserves the intrinsic image feature, instead of directly transforming the
detection task into a matrix decomposition problem. Extensive experiments
substantiate the effectiveness of our deep unfolding framework and demonstrate
its trustworthy results, surpassing baseline methods in both qualitative and
quantitative evaluations.
