Generating High-Resolution SAR Images with Loss Function Customization
Junghoon Lee, Junwoo Kim, Jae‐Hyun Kim, Duk‐jin Kim · 2023
High-resolution SAR images have become essential data in various fields such as object detection and disaster analysis. However, obtaining high-resolution images is relatively difficult and expensive, leading to ongoing research on methods to generate them. Recently, deep learning has been explored to convert low-resolution images into high-resolution ones. In this paper, we utilize deep learning to generate high-resolution images, and we modify and combine loss functions to enhance the model’s performance. The employed loss function incorporates various functions such as SSIM, MS-SSIM, L1, and L2. The experimental results demonstrate that utilizing the combined loss function outperforms using the existing loss function alone for image generation. When comparing the indicators, the combined loss function yields an SSIM value of 0.6098 and a DISTS value of 0.0542, indicating a 5% improvement over the original loss function.