Computing and Informatics

In the realm of single-image super-resolution (SISR), generating high-resolution (HR) images from a low-resolution (LR) input remains a challenging task. While deep neural networks have shown promising results, they often require significant computational resources. To address this issue, we introduce a lightweight convolutional neural network, named BTAN, that leverages the connection between LR and HR images to enhance performance without increasing the number of parameters. Our approach includes a target transform module that adjusts output features to match the target distribution and improve reconstruction quality, as well as a spatial and channel-wise attention module that modulates feature maps based on visual attention at multiple layers. We demonstrate the effectiveness of our approach on four benchmark datasets, showcasing superior accuracy, efficiency, and visual quality when compared to state-of-the-art methods.