# Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection

## TL;DR

This paper introduces a novel infrared patch-tensor model that combines local and non-local priors for improved single-frame small target detection in complex backgrounds, outperforming existing methods especially with dim targets and clutter.

## Contribution

It proposes a reweighted infrared patch-tensor model utilizing both local structure and non-local self-correlation priors with an adaptive weighting scheme for enhanced detection.

## Key findings

- Outperforms state-of-the-art methods on challenging images
- Effective in detecting very dim targets and heavy clutter
- Utilizes a reweighted robust PCA approach with adaptive weights

## 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.