We propose a time-of-flight ranging method by measuring the pulse laser phase based on field-programmable gate array (FPGA) digital mixing, in which accurate laser ranging with a total digital system is realized by combining one FPGA chip with a simple laser transmitting and receiving circuit. The system has the advantage of simple structure, reliable operation, low manufacturing cost and easy integration. Experimental results show that compared with the pulse laser ranging method using commercial time-to-digital converter chips, it can realize precise measurement of distance, and the measurement error can be significantly reduced. In addition, this method has better performance of ranging accuracy.

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.

In iterative pseudo-inverse ghost imaging (IPGI), how much the noise interference item of the current iteration approximates the real noise greatly depends on the clarity of initial image. In order to improve IPGI, we propose a method that introduces anisotropic diffusion to construct a more accurate noise interference term, where anisotropic diffusion adapts to both the image and the noise, so that it balances the tradeoff between noise removal and preservation of image details. In our algorithm, the anisotropic diffusion equation is used to denoise the result of each iteration, then the denoised image is used to construct the noise interference term for the next iteration. Compared to IPGI, our method has better performance in visual effects and imaging quality, as the image edges and details are better preserved according to the experimental results.