Research on Intelligent Correction System of 3D Image Reconstruction Error Based on Deep Neural Network

In order to improve the accuracy and stability of 3D image reconstruction, an intelligent correction system based on generative adversarial network (GAN), convolutional neural network (CNN) and Bat algorithm (BA) is proposed. As a powerful generative model, generative adversarial network can effectively generate high-quality images through adversarial training mechanism, while convolutional neural network is excellent in feature extraction. Combining these two advantages, we can build a generator and discriminator with excellent performance for achieving high-quality 3D image reconstruction. By simulating the echolocation behavior of bats in the process of hunting, the bat algorithm can find the optimal parameters in the complex solution space, thus accelerating the convergence of the model and improving the reconstruction accuracy. In the process of model training, the Bat algorithm constantly adjusts the learning rate, batch size and other hyperparameters to adapt to different reconstruction scenarios and improve the overall performance. Through experimental verification, the proposed model shows superior performance in 3D image reconstruction tasks, which is significantly superior to traditional methods and other deep learning models. The experimental results show that the intelligent correction system can effectively reduce the reconstruction error and improve the image quality, which provides a new idea and method for the research of 3D image reconstruction.