Reconstruction of Complex Scene Radiation Fields Based on Image Restoration Equation

Abstract In the context of nuclear safety production, the detailed information on three-dimensional radiation field dose rates plays a vital role in enhancing radiation protection and planning for radioactive areas. Research on inversion methods, especially under scenarios of sparse or irregular measurement points, has attracted significant attention. Currently, there are advances in inversion methods based on interpolation algorithms and neural network techniques. Interpolation algorithms are inversion methods capable of rapidly inverting and reconstructing the distribution of radiation field dose rates with limited radiation field data. These methods include grid data interpolation, finite element methods, inverse distance weighting, kriging, and radial basis function methods, and are the most widely used methods for the quick reconstruction of radiation fields at present. Based on the information of known sample points, these algorithms predict information at unsampled points by selecting appropriate analytical models to find suitable mapping relationships. They offer the advantages of simplicity and speed, but have poor capability in restoring the geometric shapes of multiple radiation sources and face significant challenges in the direct inversion and reconstruction of three-dimensional radiation fields. In some cases, sparse sampling data can lead to suboptimal inversion results. Neural network algorithms, renowned for their strong nonlinear mapping capabilities, can approximate complex functions by learning from extensive data sets. Once appropriately trained, neural networks can process incomplete or uncertain knowledge and predict outcomes based on limited data. These algorithms are commonly used in image restoration and inverse problem-solving. In recent radiation field dose rate inversion methods, neural network algorithms have gained attention due to their exceptional data fitting and predictive capabilities. In recent years, research has proposed an inversion method that combines interpolation with image restoration algorithms. This method utilizes the smoothness of high-order diffusion restoration equations. Through iterative solving of diffusion equations, it progressively restores the dose rate information of the three-dimensional radiation field, thus achieving rapid inversion of the radiation field dose rate distribution. This method has proven its superior performance in simple scenarios, but its efficacy in real and complex environments lacks verification. This paper implements the diffusion restoration equation for radiation field dose rate inversion in complex scenarios, validating its reliability in environments with multiple shields and materials, thereby expanding the method’s applicability. This advancement represents a significant step in the field of radiation protection, offering an effective way to reduce radiation-related risks by rapidly reconstructing accurate radiation fields in complex settings. Moreover, the integration of traditional interpolation methods with image restoration concepts highlights the potential of innovative hybrid solutions in addressing complex scientific challenges, providing a novel approach for radiation field reconstruction.