Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm's effectiveness in this paper.

Automated summary

A new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model is proposed in this study to improve the detection capability of dim and small targets in non-smooth scenes. Experimental results demonstrate that the method performs better in all six sequential image scenes than the traditional algorithm, and the detection results validate the effectiveness of the algorithm.