In the case of a certain wavelength, digital holographic imaging is larger than other imaging methods can obtain finer target structures. Therefore, terahertz digital holography technology has received more and more attention. Among them, the image segmentation of reproduced images has important application value. The training set is very important in image segmentation based on convolutional neural network; and currently there are not enough terahertz digital holographic images, and there is no standard training set for real images. For this reason, this article self-made a 2.52THz simulation reconstruction image based on angular spectrum and phase retrieval algorithm as a training set, the image size of which is 256×256. The test uses a real 124×124 terahertz reconstructed image, and expands to 256×256; and objectively evaluates the segmentation results at this image resolution. Compared with other training sets, the results show that it can be better segmented by using the data set established in this paper.
Terahertz imaging of occluded objects is a popular technology, which is an important feature superior to visible light imaging. However, the quality of the target image is obviously damaged due to factors such as discontinuity and absorption of the occlusion, which makes it difficult to segment the image. It is especially difficult to segment digital holographic reconstruction images of small objects with high resolution. In this paper, clustering segmentation algorithms are compared for terahertz images of metal objects which is occluded by paper. K-means, fuzzy C-means (FCM) and fuzzy c-means clustering with spatial constraints (FCM-S) algorithms were used respectively. Since the minimum horizontal target is only three pixels, the mean template size in these algorithms is all 3*3. The experimental results show that FCM-S of the segmentation effect obtained is the best among the three algorithms, because FCM-S considers the pixel neighborhood information.
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