AI-Driven Error Correction for Real-Time Wireless Image Transmission: Integrating Banach Space Principles into Deep Learning

Wireless communication has been widely adopted owing to its flexibility. However, it introduces many limitations, such as noise interference for real-time image transmission, which is key to modern applications, such as telemedicine. This study seeks to integrate Banach space principles with deep learning to improve the error correction in wireless image transmission. This research is justified by the need to maintain high-quality images in real time, despite unpredictable wireless channel errors. They proposed a convolutional autoencoder enhanced with an iterative refinement module that enforces contraction mappings via spectral normalization and contraction regularization. The model was trained on the CIFAR-10 dataset using noise simulation and advanced data augmentation, and evaluated using Peak Signal to Noise Ration(PSNR), Structural Similarity Index (SSIM), and inference time metrics. The experiments including dynamic lambda scheduling, demonstrated that under moderate Gaussian noise the model achieves up to 23.66 dB PSNR and 0.8489 SSIM while processing over 600 frames per second. Ethical considerations were addressed using publicly available data, and the code and methodology are well documented for reproducibility. Study limitations included the use of low-resolution images and simulated noise, which may not fully capture real-world challenging conditions. Future work will extend these results to larger and more complex datasets and real transmission scenarios.