In this paper, a quadtree-based non-local means image denoising method for terahertz images is used. Firstly, the noise image is decomposed by quadtree to obtain different size sub-blocks, which makes better use of the non-local selfsimilarity of the image. Then, the non-local means filter based on bisquare weighting function is used to denoise each sub-block to improve the quality of the reconstructed image. Finally, the sub-blocks are aggregated to get a complete denoised image. The experimental results of terahertz image denoising show that the method can preserve the details of the image, effectively remove the background noise caused by the imaging system, and has a good denoising effect on terahertz image.
Terahertz digital holographic reconstructed images are vulnerable to noise pollution. This paper uses neural network to segment terahertz image, because this method is insensitive to noise. Firstly, the training sample image is decomposed into several sub-images, and the backward propagation(BP) neural network is trained by them. At the same time, the optimal number of hidden layer neurons is selected. Then the trained neural network is applied to the segmentation of terahertz image. Different segmentation results are obtained by changing the variance of noise in the training sample image. The best segmentation results and training samples are determined by using the mean structural similarity(MSSIM). Finally, compared with the classical image segmentation algorithm, the results show that the segmentation effect of the neural network is better.
A composite denoising method based on non-local means filter, Lucy-Richard algorithm and adaptive histogram equalization is proposed for terahertz reflection scanning images. Firstly, adaptive histogram equalization is used to adjust the brightness of the image based on Lucy-Richard restoration to improve the image definition, and finally the quality of the reconstructed image is further improved by using the non-local means algorithm based on bisquare weighting function. The experimental results of terahertz image denoising show that the proposed method can preserve the details of the image, effectively remove the background noise caused by the laser output fluctuation in the imaging system, and restore the blurred image, which has a good denoising effect on terahertz image.