article-journal

This study presents DenoDet, a novel synthetic aperture radar (SAR) target detection network that leverages frequency domain transform for multi-scale subspace representation. The proposed TransDeno module dynamically denoises across subspaces by preserving target signals and attenuating noise, and a deformable group fully-connected layer (DeGroFC) adjusts the granularity of subspace processing based on input features.

A Sparse Differential Directionality prior (SDD) framework is proposed for infrared small target detection. SDD leverages directional characteristics to differentiate targets from background, applying mixed sparse constraints on differential directional images and continuity difference matrix derived from Tucker decomposition. Saliency coherence strategy further enhances target detectability during hierarchical decomposition.

This paper introduces SeRankDet, a deep learning model for infrared small target detection. SeRankDet leverages our novel Selective Rank-Aware Attention module for preserving salient responses, Large Selective Feature Fusion for dynamic feature integration, and Dilated Difference Convolution for improved target-background separation. Despite its lightweight design, SeRankDet delivers state-of-the-art performance.

We propose OSCAR, a one-stage cascade refinement network for single-frame infrared small target detection, along with a new benchmark consisting of the SIRST-V2 dataset, normalized contrast evaluation metric, and DeepInfrared toolkit.

This paper proposes a model-driven deep network that combines discriminative networks and conventional model-driven methods, utilizing a feature map cyclic shift scheme and bottom-up attentional modulation to highlight and preserve infrared small target features.

This paper proposes a novel method for infrared small target detection in heterogeneous backgrounds. It employs an infrared patch-tensor (IPT) model and leverages both local and non-local priors. The target-background separation is modeled as a robust low-rank tensor recovery problem, enhanced with an entry-wise local-structure-adaptive and sparsity enhancing weight.