FMDL-Net: Fourier modulation and dynamic mixing for light field image super-resolution

Light field imaging suffers from an inherent spatial-angular resolution trade-off. While deep learning has advanced light field image super-resolution (LFISR), convolutional neural networks (CNNs) are limited by local receptive fields that fail to capture long-range angular dependencies, and Vision Transformers introduce quadratic complexity and high-frequency detail loss. We propose FMDL-Net, a disentangled network that integrates Fourier Modulated Attention (FMA) and Dynamic Mixing Layers (DML) to address these challenges. FMA leverages the Fast Fourier Transform to achieve efficient global receptive fields and maintain geometric consistency, while DML employs content-adaptive dynamic convolutions to preserve complex textures and handle varying disparities. Experiments on five public datasets demonstrate that FMDL-Net outperforms state-of-the-art methods in PSNR and SSIM, with fewer parameters and lower computational cost.