Research on multiscale feature fusion and adaptive optimization method for complex light field reconstruction

Abstract

Complex light field reconstruction requires recovering high-resolution light field data with spatial clarity, angular consistency, and occlusion boundary stability under limited viewpoint sampling conditions. This is a key issue in computational imaging, free-viewpoint display, 3D perception, and immersive interaction. Existing depth reconstruction methods can usually improve detail representation by utilizing complementary information between adjacent viewpoints. However, when sparse viewpoints, large parallax, weak texture, non-uniform noise, and occlusion boundaries coexist, viewpoint drift, edge blurring, and high-frequency texture loss are still prone to occur. To address these issues, this paper proposes a multi-scale feature fusion and adaptive optimization method, MSAF-AO, for complex light field reconstruction. This method first constructs a unified spatial-angle input representation, mapping sparse sub-aperture images to a shared feature domain. Then, it uses multi-scale feature branches to capture local texture, edge contours, and global parallax structure respectively. Furthermore, it designs adaptive fusion weights for occlusion awareness, enabling features of different scales to dynamically participate in reconstruction according to regional complexity. Finally, it constructs a joint objective function composed of reconstruction error, angular consistency, edge preservation, and fusion regularization. Experimental results show that MSAF-AO achieves higher PSNR, SSIM, and lower angular consistency error under complex parallax and noise conditions, and has more stable structural recovery capability in occluded areas.