为了解决重构图像中局部微小区域的成像质量问题,提出一种基于区域分割的压缩计算鬼成像方法。先获取复杂物体表面粗略轮廓的感兴趣区域(ROI),同时运用阈值分割方法进行边缘检测以提取图像中不感兴趣区域(N-ROI),并根据识别区域生成相应大小的随机散斑图;再结合压缩感知技术和二阶计算关联算法分别恢复分割的子图像,最后通过图像拼接技术对图像进行复原。实验结果表明,当采样3000次时,所提方法的峰值信噪比较传统计算鬼成像方法有超过9 dB的提升,且比采样500次时增加了约49.57%。该方法可解决其他传统方法中图像局部微小区域成像质量较差的问题,不仅能够大大减少采样数和目标区域空间强度运算量,同时显著提高了图像微小局部区域的成像质量,为关联成像方式提供了一种新的方案。

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 subimages are 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.