Efficiency Enhanced Super Resolution Generative Adversarial Network via Advanced Knowledge Distillati

초록

Super-resolution (SR) stands as a prominent challenge in computer vision with diverse applications. Generative adversarial networks (GANs) yield impressive SR outcomes by restoring high-quality images from low-resolution input. However, GAN-based SR (particularly generators) have high memory demands, leading to performance degradation and energy consumption, making them unsuitable for resource-limited devices. Addressing this concern, our paper introduces a novel and efficient SR-GAN (generator) model architecture by strategically leveraging knowledge distillation, which results in reducing storage demands by 58% while enhancing performance. Our approach involves extracting feature maps from a resource intensive model to design a lightweight model with minimal computational and memory requirements. Experiments across several benchmarks demonstrate that the proposed compressed model outperforms existing knowledge distillation-based techniques, particularly in regard to SSIM, PSNR, and overall image quality in x4 super-resolution tasks. In the future, this compressed model will be implemented and benchmarked with existing models in resource-limited devices such as tablet and wearing devices.

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