Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection (1703.09157v1)

Abstract: Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.

• The paper presents a novel infrared patch-tensor approach that integrates local and non-local priors to robustly separate targets from clutter.
• It employs a local structure-adaptive weighting and sparse reweighting scheme to enhance target-background separation in heterogeneous images.
• Empirical results demonstrate that the method outperforms state-of-the-art techniques, achieving higher signal-to-noise ratios and improved detection probabilities.

An Evaluation of the Reweighted Infrared Patch-Tensor Model for Single-Frame Small Target Detection

The field of infrared small target detection presents significant challenges, particularly within highly heterogeneous backgrounds where traditional methods struggle. The paper "Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection" by Yimian Dai and Yiquan Wu introduces an innovative approach that incorporates both local and non-local priors to improve detection accuracy in these complex scenarios.

The authors propose a novel Infrared Patch-Tensor (IPT) model that efficiently represents images and retains spatial correlations by modeling the target-background separation as a robust low-rank tensor recovery problem. This approach diverges from traditional methods that often fail to perform well in the presence of strong edges and other interfering components due to insufficient leveraging of available priors.

The Key Components of the Model

1. Infrared Patch-Tensor Representation: The proposed method generalizes the patch-image model by constructing a patch-tensor, capturing additional spatial correlations across multiple unfoldings, which are revealed through the analysis of singular values. This comprehensive representation allows for more effective low-rank and sparse decomposition essential for the separation task.
2. Incorporation of Local Structure Priors: Using the structure tensor, an entry-wise local-structure-adaptive weight is designed, enhancing the model's ability to discriminate between targets and non-target edge structures. This weight adapts locally based on the image's structural information, thus improving the robustness of target detection against cluttered backgrounds.
3. Sparsity Enhancement: Applying an adaptive reweighting scheme further enforces the sparsity of the target tensor, effectively distinguishing true target signals from noise and non-target structures. This mechanism, inspired by reweighted $\ell_1$ minimization strategies, supports faster convergence and reduces computational overhead by stopping iteration when the non-zero entry count stabilizes.

Empirical Validation and Performance

Extensive experiments conducted on various infrared image datasets reveal that the Reweighted Infrared Patch-Tensor (RIPT) model surpasses contemporary state-of-the-art methods in performance, particularly in scenarios involving very dim targets and complex background clutters. The model consistently achieves higher scores in metrics such as local signal-to-noise ratio gain, background suppression factor, and signal-to-clutter ratio gain.

Moreover, the ROC analysis underlines the efficacy of the proposed model, demonstrating superior detection probability at comparable false alarm rates across multiple challenging sequences. The proposed approach's robustness against varying noise levels further highlights its practical applicability in real-world conditions where other models frequently underperform.

Implications and Future Directions

The integration of both non-local and local priors into the RIPT model illustrates a forward step in enhancing the reliability and accuracy of infrared small target detection. This work suggests potential extensions to multi-frame scenarios or real-time applications, where rapid inference is crucial. Future enhancements may focus on optimizing algorithmic complexity further for operational deployment and extending the framework to other imaging modalities or combined spectral-spatial analyses.

In conclusion, the paper offers a substantial contribution to the domain of small target detection, providing both theoretical insights and a robust methodological framework that addresses the limitations faced by existing techniques. The introduction of dual prior integration sets the groundwork for continued advancements in the detection and identification capabilities of automated surveillance systems.