The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, e.g., at fluctuating air–water interfaces. This challenge is particularly pronounced in technical energy conversion processes such as bubble formation in electrolysis, droplet formation in fuel cells, or film flows. In this paper, a flow field estimation algorithm that can perform the aberration correction function is proposed, which integrates the flow field distribution estimation algorithm based on the Particle Image Velocimetry (PIV) technique and the novel actuator-free adaptive optics technique. Two different multi-input convolutional neural network (CNN) structures are established, with two frames of distorted PIV images and measured wavefront distortion information as inputs. The corrected flow field results are directly output, which are divided into two types based on different network structures: dense estimation and sparse estimation. Based on a series of models, a corresponding dataset synthesis model is established to generate training datasets. Finally, the algorithm performance is evaluated from different perspectives. Compared with traditional algorithms, the two proposed algorithms achieves reductions in the root mean square value of velocity residual error by 84% and 89%, respectively. By integrating both flow field measurement and novel adaptive optics technique into deep CNNs, this method lays a foundation for future research aimed at exploring more intricate distortion phenomena in flow field measurement.