Pseudo-Sample Generation and Self-Supervised Framework for Infrared Dim and Small Target Detection.

ABSTRACT: Infrared dim and small target detection is crucial for long-range sensing. However, its deep representation learning is severely constrained by the scarcity of accurately annotated real data, and related research remains underdeveloped. Existing data generation methods based on patch synthesis or geometric transformations fail to incorporate the physical degradation mechanisms of infrared imaging systems and reasonable environmental constraints, leading to significant discrepancies between synthetic data and real-world scenarios. To address this issue, this paper proposes a novel pseudo-sample generation paradigm based on physics-informed degradation modeling and high-order constraints. First, we construct an infrared image degradation model that decouples the degradation processes of targ

SUBMITTER: Guo J 

PROVIDER: S-EPMC12732119 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature