A BTR-Based Approach for Detection of Infrared Small Targets

TL;DR

This paper introduces a novel infrared small target detection method using bilateral tensor ring decomposition, significantly improving detection accuracy and efficiency over existing techniques in complex dynamic backgrounds.

Contribution

The paper proposes a new BTR-ISTD model that reconstructs data into a tensor and applies bilateral tensor ring decomposition for enhanced detection of small targets.

Findings

Outperforms state-of-the-art methods in detection accuracy

Achieves better background suppression

Offers improved computational speed

Abstract

Infrared small target detection plays a crucial role in military reconnaissance and air defense systems. However,existing low-rank sparse based methods still face high computational complexity when dealing with low-contrast small targets and complex dynamic backgrounds mixed with target-like interference. To address this limitation, we reconstruct the data into a fourth-order tensor and propose a new infrared small target detection model based on bilateral tensor ring decomposition, called BTR-ISTD. The approach begins by constructing a four-dimensional infrared tensor from an image sequence, then utilizes BTR decomposition to effectively distinguish weak spatial correlations from strong temporal-patch correlations while simultaneously capturing interactions between these two components. This model is efficiently solved under the proximal alternating minimization (PAM) framework. Experimental results demonstrate that the proposed approach outperforms several state-of-the-art methods in terms of detection accuracy, background suppression capability, and computational speed.