Improving Face Image Transmission with LoRa Using a Generative Adversarial Network

Although it is a technology that can be pretty important for remote areas lacking internet or cellular data, the difficulties it presents in large data transmission prevent LoRa from developing sufficiently for image transmission. This challenge is particularly relevant for applications requiring the transfer of facial images, such as remote security or identification. It is possible to overcome these difficulties by reducing the data size through the application of various image processing methods. In the study, the face-focused enhanced super-resolution generative adversarial network (ESRGAN) is trained to address the significant quality loss in low-resolution face images transmitted to the receiver as a result of image processing techniques. Also, the trained ESRGAN model is evaluated comparatively with the Real-ESRGAN model and a standard bicubic interpolation baseline. In addition to Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, Learned Perceptual Image Patch Similarity (LPIPS) for perceptual quality and a facial identity preservation metric are used to calculate the similarities of the produced super-resolution (SR) images to the original images. The study was tested in practice, demonstrating that a facial image transmitted in 42 min via LoRa can be transmitted in 5 s using image processing techniques and that the images can be improved close to the real images at the receiver. Thus, with an integrated system that enhances the transmitted visual data, it becomes possible to transmit compressed, low-resolution image data using LoRa. The study aims to contribute to remote security or identification studies in regions with difficult internet and cellular data transmission by making significant improvements in image transmission with LoRa.