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.
A clear image of an observed object may deteriorate into unrecognizable speckle when encountering heterogeneous scattering media, thus it is necessary to recover the object image from the speckle. A method combining least square and semidefinite programming is proposed, which can be used for imaging through scattering media. The proposed method consists of two main stages, that is, media scattering characteristics (SCs) estimation and image reconstruction. SCs estimation is accomplished through LS concept after establishing a database of known object-and-speckle pairs. Image reconstruction is realized by solving an SDP problem to obtain the product of the unknown object image and its Hermitian transposition. Finally, the unknown object image can be reconstructed by extracting the largest rank-1 component of the product. Structural similarity (SSIM) index is employed as a performance indicator in speckle prediction and image reconstruction. Numerical simulations and physical experiments are performed to verify the feasibility and practicality of the proposed method. Compared with the existing phase shift interferometry mean square optimization method and the single-shot phase retrieval algorithm, the proposed method is the most precise to obtain the best reconstruction results with highest SSIM index value. The work can be used for exploring the potential applications of scattering media, especially for imaging through turbid media in biomedical, scattering property measurement, and optical image encryption.