Adaptive Pixel Classification and Equivalent Large Kernels for Lightweight Image Super-Resolution

Lightweight super-resolution (SR) has garnered attention for balancing performance and efficiency in resource-constrained environments. In lightweight SR tasks, traditional CNN-based methods are constrained by limited receptive fields, leading to suboptimal SR performance. In contrast, ViT-based models achieve remarkable results but suffer from significant computational burden due to the self-attention mechanism. In this paper, we propose adaptive Pixel Classification and equivalent Large Kernels Network (PCLKN), a novel lightweight SR model that addresses the limitations of traditional CNN-based and ViT-based methods. PCLKN utilizes equivalent large kernels to expand the receptive field while relying solely on convolutional operations, significantly reducing computational overhead. Additionally, it integrates global priors with spatial and channel attention to enhance feature extraction and leverages adaptive pixel classification to utilize similar pixel information for reconstruction. Experimental results on benchmark datasets demonstrate that PCLKN achieves superior SR performance with an excellent trade-off between performance and computational complexity.