The robustness of the particle swarm system is great, which is very helpful for solving ill?conditioned problems such as image reconstruction. However, the large number of pixels in the reconstructed image leads to a large dimension of particle and it is difficult for the particle to achieve the optimal solution in the optimization process. In order to solve this problem, a constraint is added to the particle position, imaging by Tikhonov regularization algorithm is used as the reference of particle position. The search for particles is constrained to the range of Tikhonov regularization algorithm reconstructs the image. Using the penalty function to solve the constraint problem to improve the particle search speed. Linearly decreasing weights as inertial weights for particle swarms optimization to realize the adaptive dynamic adjustment of the inertia weight and improve the flexibility of the algorithm; the chaotic operator is added to the position search process of the particle swarm optimization, when the particle falls into the local optimum, the chaotic variable will fluctuate within a certain range, reducing the missed rate of the optimal solution. The simulation results show that The improved particle swarm algorithm is more accurate and efficient than the traditional LBP algorithm and Tikhonov regularization algorithm.