Occlusion artifacts significantly hinder light field (LF) image reconstruction, especially in complex scenes. We propose a spectral normalized U-Net for LF occlusion removal, which begins by stacking LF views and extracting view-dependent features using a local feature encoder. To capture spatial complexity, ResASPP enable multi-scale context aggregation, while channel attention enhances occlusion-related features. Spectral normalization is applied to all convolutional layers to improve training stability and generalization. The encoder-decoder structure with skip connections preserves fine details. Experimental results show our method restores occluded regions more accurately than baselines.