Enhancing Real-Time Super Resolution with Partial Convolution and Efficient Variance Attention

With the increasing availability of devices that support ultra-high-definition (UHD) images, Single Image Super Resolution (SISR) has emerged as a crucial problem in the field of computer vision. In recent years, CNN-based super resolution approaches have made significant advances, producing high-quality upscaled images. However, these methods can be computationally and memory intensive, making them impractical for real-time applications such as upscaling to UHD images. The performance and reconstruction quality may suffer due to the complexity and diversity of larger image content. Therefore, there is a need to develop efficient super resolution approaches that can meet the demands of processing high-resolution images. In this paper, we propose a simple network named PCEVAnet by constructing the PCEVA block, which leverages Partial Convolution and Efficient Variance Attention. Partial Convolution is employed to streamline the feature extraction process by minimizing memory access. And Efficient Variance Attention (EVA) captures the high-frequency information and long-range dependency via the variance and max pooling. We conduct extensive experiments to demonstrate that our model achieves a better trade-off between performance and actual running time than previous methods.