Speckle-based compressive target recognition and reconstruction through scattering media

Abstract

Scattering media would scratch light propagation, and images would degenerate into unrecognizable speckle patterns. Conventional target recognition through scattering media is composed of two steps, i.e., reconstruction and recognition. Here, combining the compressive sensing with feature extraction, a method of efficient speckle based compressive target recognition through scattering media is proposed. In the paper, autocorrelation of speckles is proved to have the same singular values as that of their corresponding objects, and then speckle based recognition is introduced. Compressive sensing can be used to retrieve signals with measurements fewer than those required by Nyquist-Shannon theory. With the proposed method, scattered object recognition can be replaced with speckle recognition, bypassing the conventional object reconstruction procedure. Performances are validated through relevant experiments. Besides, benefited from the conclusion, domain adaption based support vector regression method is proposed and utilized for imaging through scattering media then. Domain adaption is introduced to transfer leaning samples and testing samples into a new space where the distance between them is much closer, leading to high reconstruction fidelity in the followed support vector regression based inverse scattering stage. Principle component analysis is also considered to help decrease dimension and thus improving efficiency. Experiments validate that the presented technique owns a higher image reconstruction efficiency and fidelity, compared with our previous researches. Since the target recognition and reconstruction is mainly based on ground truth images, the work is valuable and meaningful for remote sensing applications, especially for object detection or monitoring when scattering is occurred.