Deep Unfolding Network via Tri-Domain Structural Learning for Light Field Image Super-Resolution

Light field image super-resolution (LFSR) aims to reconstruct high-resolution LF images from low-resolution observations while preserving geometric structures. However, existing model-based and deep learning-based methods cannot simultaneously achieve rigorous mathematical modeling and powerful feature representation. To address this issue, we propose a deep unfolding network via tri-domain structural learning (LF-DUTS). Specifically, we formulate a 4D LF degradation model under the maximum a posteriori framework and unfold its optimization process into an interpretable multi-stage network. We further design a tri-domain structural refinement group to jointly exploit texture, multi-view, and disparity information from the spatial, angular, and epipolar plane image domains. In addition, we develop a cross-stage feature fusion module to enable feature interaction across unfolding stages. Experimental results on multiple benchmark datasets demonstrate that LF-DUTS consistently outperforms state-of-the-art methods, producing sharper textures, and better disparity consistency.