Overcoming occlusions in light field (LF) imaging is a challenging yet complex task crucial for scene understanding, image quality enhancement, and restoring visual details in obstructed scenes. This review examines contemporary occlusion removal methods, spanning from classical techniques to advanced deep learning approaches that leverage LF data’s spatial and angular dimensions. We categorize these methods into two domains: (1) single-view inpainting methods often adapted for LF contexts, and (2) specialized LF occlusion removal techniques that exploit multi-view data. The review explores how these methods mitigate occlusion artifacts and also investigates LF acquisition technologies, representations, and the role of loss functions in optimizing model performance. A discussion of publicly available datasets and performance evaluation metrics addresses the challenges of handling large occlusions. The review concludes with future research directions, emphasizing hybrid approaches, refined loss functions, and scalable solutions for LF occlusion removal.

Industrial defect classification remains a crucial challenge due to the visual complexity, rarity, and diversity of defect types encountered in manufacturing. In this paper, we present a hybrid deep learning framework that integrates YOLOv11 and EfficientNet-B7 to perform robust multi-class defect classification. Our model combines the semantic richness of YOLO’s spatial features with the fine-grained representation power of EfficientNet, further enhanced by a Convolutional Block Attention Module (CBAM) and a lightweight Feature Pyramid Network (FPN) for attention-guided multi-scale refinement. Unlike conventional anomaly detectors or class-specific models, our framework supports unified classification across diverse object categories and defect types. We evaluate the proposed model on two datasets: the MVTec-FS benchmark, which includes 46 defect types across 14 industrial categories, and our proprietary Window dataset, which comprises three real-world defect classes under variable conditions. The model achieves a state-of-the-art accuracy of 91.90% on MVTec-FS and 96.13% on the Window dataset, outperforming existing CNN, transformer, and ensemble baselines. Ablation studies further demonstrate the incremental contribution of each module to the overall performance. These results validate our model’s ability to generalize across domains and offer practical utility in industrial quality control pipelines.

Large vision–language models (VLMs) can describe images fluently, yet their anomaly decisions often rely on opaque heuristics and manual thresholds. We present ThinkAnomaly, a rationale-first vision–language framework for industrial anomaly detection. The model generates a concise structured rationale and then issues a calibrated yes/no decision, eliminating per-class thresholds. To supervise reasoning, we construct chain-of-thought annotations for MVTec-AD and VisA via synthesis, automatic filtering, and human validation. We fine-tune Llama-3.2-Vision with a two-stage objective and a rationale–label consistency loss, yielding state-of-the-art classification accuracy while maintaining a competitive detection AUC: MVTec-AD—93.9% accuracy and 93.8 Image-AUC; VisA—90.3% accuracy and 85.0 Image-AUC. This improves classification accuracy over AnomalyGPT by +7.8 (MVTec-AD) and +12.9 (VisA) percentage points. The explicit reasoning and calibrated decisions make ThinkAnomaly transparent and deployment-ready for industrial inspection.

Light-field (LF) imaging transforms occlusion removal by using multiview data to reconstruct hidden regions, overcoming the limitations of single-view methods. However, this advanced capability often comes at the cost of increased computational complexity. To overcome this, we propose the U²-LFOR network, an end-to-end neural network designed to remove occlusions in LF images without compromising performance, addressing the inherent complexity of LF imaging while ensuring practical applicability. The architecture employs Residual Atrous Spatial Pyramid Pooling (ResASPP) at the feature extractor to expand the receptive field, capture localized multiscale features, and enable deep feature learning with efficient aggregation. A two-stage U²-Net structure enhances hierarchical feature learning while maintaining a compact design, ensuring accurate context recovery. A dedicated refinement module, using two cascaded residual blocks (ResBlock), restores fine details to the occluded regions. Experimental results demonstrate its competitive performance, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 29.27 dB and Structural Similarity Index Measure (SSIM) of 0.875, which are two widely used metrics for evaluating reconstruction fidelity and perceptual quality, on both synthetic and real-world LF datasets, confirming its effectiveness in accurate occlusion removal.