Compressive Computational Ghost Imaging Method Based on Region Segmentation

In this study, we propose a compressive computational ghost imaging method based on region segmentation to solve imaging quality problems in local micro-regions of reconstructed images. First, a rough-contour region of interest (ROI) on the surface of a complex object is obtained and a threshold segmentation method is used to perform an edge detection to extract the no-region of interest (N-ROI) in an image and generate random speckle patterns of the corresponding size based on the recognition area. Then, compressed subimagcs arc restored by combining a compressed sensing technology and the second-order computational ghost imaging algorithm. Finally, an image stitching technique is adopted to restore the image. Experimental results show that when the number of samples is 3000, the peak signal-to-noise ratio of the proposed method is improved by more than 9 dB compared with that by traditional computational ghost imaging methods, and it is increased by approximately 49.57% compared with that when the number of samples is 500. The proposed method can solve local micro-region imaging quality problems in reconstructed images, which can not only greatly reduce the number of samples and the spatial intensity calculation of the target region but can also significantly improve the imaging quality of the local micro-region of an image, providing a new solution for correlation imaging.