Optimization of retina-like illumination patterns in ghost imaging

Abstract

Ghost imaging (GI) reconstructs images using a single-pixel or bucket detector, which has the advantages of scattering robustness, wide spectrum, and beyond-visual-field imaging. However, this technique needs large amounts of measurements to obtain a sharp image. Numerous methods are proposed to overcome this disadvantage. Retina-like patterns, as one of the compressive sensing approaches, enhance the imaging quality of the region of interest (ROI) while maintaining measurements. The design of the retina-like patterns determines the performance of the ROI in the reconstructed image. Unlike the conventional method to fill in ROI with random patterns, optimizing retina-like patterns by filling in the ROI with the patterns containing the sparsity prior of objects is proposed. The proposed method is then verified by simulations and experiments compared with conventional GI, retina-like GI, and GI using patterns optimized by principal component analysis. The method using optimized retina-like patterns obtains the best imaging quality in ROI among other methods. Meanwhile, the good generalization capability of the optimized retina-like pattern is also verified. The feature information of the target can be obtained while designing the size and position of the ROI of retina-like patterns to optimize the ROI pattern. The proposed method facilitates the realization of high-quality GI.