Infrared object detection via patch‐tensor model and image denoising based on weighted truncated Schatten‐p norm minimization

Abstract

AbstractThe nuclear norm minimization (NNM) is a special non‐convex rank minimization convex relaxation scheme for image denoising and object detection that requires denoising and background subtraction. Considering excessive shrinkage of rank components and equal treatment of different rank components, NNM is extended to the weighted Schatten‐p norm minimization (WSNM) with weights assigned to different singular values. In this paper, a multi‐channel weighted truncated WSNM model based on the WSNM optimization framework is proposed for RGB colour image denoising. On the basis of different noise intensities and non‐local self‐similar patches of each channel of the colour image itself, the proposed model is improved significantly by the optimization methods of superposition and truncation. Meanwhile, it can be generalized to the tensor space and employed to the infrared imaging target detection based on the spatial‐temporal tensor model for the first time. And the efficient alternating direction multiplier‐based algorithms are developed to solve the proposed model and the accuracy of the algorithm is effectively improved by choosing an adaptive threshold. Extensive experiments on real infrared data verified the proposed method state‐of‐the‐arts and effectiveness.