Joint Constraint Based on γ-Norm and TV-Sparse for Infrared Dim Small Target Detection

Abstract

Aiming at the problem that the ability of infrared dim small target detection algorithm based on traditional infrared patchimage (IPI) model to suppress background clutter is not strong, a new detection model (γ-TSIPI) based on γ-norm, total variational regularization, and sparse constraint modeling is proposed. Firstly, the original infrared image is transformed into an IPI, and then the γ-norm and total variational regularization are used to constrain the background patch image to reduce the residual noise in the target image. At the same time, the edge information of the image is retained to avoid excessive smoothness of the restored background image. In addition, considering that the L1 norm in the traditional IPI model may reduce the dim small target excessively, the weighted L1 norm is introduced to improve the recovery ability of γ-TSIPI model. Finally, the Lagrange multiplier method is applied to solve the γ-TSIPI model. Experimental results show that the proposed method can suppress background clutter better, reduce false alarm rate and effectively improve detection performance.

Keywords

• |infrared|dim small target detection|infrared patchimage(ipi)model|single frame image|γ-norm|total variation|sparse