BCSCN: Reducing Domain Gap through Bézier Curve basis-based Sparse Coding Network for Single-Image Super-Resolution

Single Image Super-Resolution (SISR) is a pivotal challenge in computer vision, aiming to restore high-resolution (HR) images from their low-resolution (LR) counterparts. The presence of diverse degradation kernels creates a significant domain gap, limiting the effective generalization of models in real-world scenarios. This study introduces the Bézier Curve basis-based Sparse Coding Network (BCSCN), a preprocessing network designed to mitigate input distribution discrepancies between the training and testing phases of super-resolution networks. BCSCN achieves this by removing visual defects associated with the degradation kernel in LR images, such as artifacts, residual structures, and noise. Additionally, we propose a set of rewards to guide the search for basis coefficients in BCSCN, enhancing the preservation of main content while eliminating information related to degradation. The experimental results highlight the importance of BCSCN, showcasing its capacity to effectively reduce domain gaps and enhance the generalization of super-resolution networks.