HFDDLap: High-Low Frequency Differentiation Dynamic Laplacian Pyramid Network for Image Super-Resolution

Existing single image super-resolution (SR) algorithms based on convolution neural networks (CNNs) have achieved usable visual results.However, they often encounter artifacts and blurring, particularly at large scaling factors (e.g., 4, 8), due to significant loss of high-frequency information.To address these challenges, we propose a novel high-low frequency differentiation dynamic Laplacian pyramid network (HFDDLap).Our approach introduces a learnable high-low frequency differentiation convolution (HLC) within high-low frequency differentiation residual channel attention blocks (HL-RCAB) to effectively capture and differentiate high-and low-frequency components, enhancing detail preservation.Additionally, we employ a dynamic deconvolution (DDC) that adaptively generates upsampling kernels based on input features, improving reconstruction accuracy by reducing feature distortion.Extensive experiments on 4 and 8 SR demonstrate that our proposed method effectively reconstructs edge details, produces satisfactory SR results and outperforms some state-of-the-art (SOTA) methods in terms of evaluation metrics.