Self-Convolutional Attention-Based Uncertainty-Aware Network for Single-Image Super-Resolution

Current super-resolution (SR) algorithms rely heavily on annotated data and often ignore the uncertainty in image degradation and features, limiting their real-world application. We propose an uncertainty-aware SR network using a self-convolutional attention mechanism. Our approach focuses on an SR reconstruction network enhanced by a cross-scale self-convolutional attention mechanism within the Transformer framework, which leverages local regions at various resolutions as convolution kernels to enhance high-frequency details. We also design a heteroscedastic uncertainty loss function to learn pixel and feature uncertainties, guiding the network to improve textures and edges adaptively. Extensive experiments show that our method achieves superior visual reconstruction on standard real-world datasets.