Reparameterizable large kernel attention networks for infrared image super-resolution

Abstract

Abstract To address the challenge of balancing reconstruction performance and inference speed in the existing infrared image super-resolution algorithms, this paper introduces a novel Large Kernel Reparameterization Attention mechanism. Based on this, we propose the reparameterizable large kernel attention network for infrared image super-resolution. During training, a multi-branch large kernel network is employed to fully extract information, while at inference time, it is equivalently transformed into a single-branch large kernel network, achieving a trade-off between processing performance and inference speed. Compared to state-of-the-art methods, our approach improves the average PSNR on a self-constructed infrared dataset by 0.0008 dB. Additionally, on the RK3588 Neural Processing Unit, it requires only 37ms to perform 4 $$\times$$ super-resolution on 320 $$\times$$ 180 images.