An Improved Whale Optimization Algorithm (IWOA) -BP neural network image restoration model was proposed to solve the problem of obvious lag in the process of restoring degraded images by traditional restoration algo- rithms. First, the uniformity and diversity of the initial population were enhanced by Tent chaos. Secondly, nonlinear weights and improved convergence factors are used to balance the global search and local optimization capabilities of the algorithm. Finally, the Levy flight strategy is combined to update the individual position to help the algorithm es- cape the local optimal. Then the IWOA-BP model is established by using the classical image data. PSNR, SSIM and NMSE were selected as the evaluation indexes of the network model, and compared with BP, GWO-BP and WOA- BP. The experimental results show that IWOA-BP model has better visual effect and improves the quality of image res- toration.