Abstract
ABSTRACT In battlefield environments, drones depend on high-resolution imagery for critical tasks such as target identification and situational awareness. However, acquiring clear images of distant targets presents a significant challenge. To address this, we propose a supervised learning approach for image super-resolution. Our network architecture builds upon the U-Net framework, incorporating enhancements to the encoder and decoder through techniques such as Discrete Wavelet Transform, Channel Attention Residual Modules, Selective Kernel Feature Fusion, Weight Normalization, and Dropout. We evaluate our model on a super-resolution dataset and compare its performance against other networks, highlighting the importance of minimizing trainable parameters for real-time deployment on resource-constrained drone platforms. The effWicacy of our proposed network is further validated through image recognition tasks and real-world scenario testing. By enhancing image clarity at extended ranges, our approach enables drones to detect adversaries earlier, facilitating proactive countermeasures and improving mission success rates
Published Version
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