Hybrid CNN-SSM Network for Efficient and Perceptually Enhanced Super-Resolution

This paper introduces HybSR, a novel image super- resolution method that leverages the strengths of both Convolutional Neural Networks (CNNs) and State Space Models (SSMs) within a hybrid architecture. CNNs effectively capture local image details, while SSMs offer linear scalability and excel at modeling long-range dependencies. By combining these two paradigms, HybSR achieves a compelling balance between efficiency and performance. Trained with a hybrid loss function encompassing pixel-wise and perceptual components, the model ensures both accurate reconstruction and high visual fidelity. Furthermore, knowledge distillation from a larger teacher network to a smaller student network enhances the model's efficiency. Ex- tensive evaluations on benchmark datasets demonstrate HybSR's superior performance in terms of reconstruction quality, model size, and computational efficiency compared to existing efficient super-resolution methods.