RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection

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  • Fengyi Wu
  • Tianfang Zhang
  • Li, Lei
  • Yian Huang
  • Zhenming Peng
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. Our source code is available at https://github.com/fengyiwu98/RPCANet.
Original languageEnglish
Title of host publication2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Publication date2024
Publication statusPublished - 2024
EventWACV 2024 - IEEE/CVF Winter Conference on Applications of Computer Vision - Waikola, Hawaii, United States
Duration: 4 Jan 20248 Jan 2024


ConferenceWACV 2024 - IEEE/CVF Winter Conference on Applications of Computer Vision
LandUnited States
ByWaikola, Hawaii

ID: 378940371