Tables are everywhere, from scientific journals, articles, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition.

Dense multi-view image reconstruction has been a focal point of research for an extended period, with recent surges in interest. The utilization of multi-view images offers solutions to numerous challenges and amplifies the effectiveness of various applications including 3D reconstruction, de-occlusion, depth sensing, saliency detection, and identifying salient objects. This paper introduces an approach to reconstructing high-density light field (LF) images, addressing the inherent challenge of balancing angular and spatial resolution caused by limited sensor resolution. We introduce an innovative approach to reconstructing LF images through a CNN-based network that combines spatial and epipolar features in both initial and deep feature extraction phases. Our network utilizes angular information during upsampling and employs dual feature extraction to effectively analyze horizontal and vertical epipolar data. Weight sharing within the CNN block between horizontal and vertically transposed stacks enhances quality while preserving model compactness. The outcomes of experiments carried out on real-world and synthetic datasets demonstrate the effectiveness of our method, showcasing its superior performance in both inference speed and reconstruction quality when compared to state-of-the-art (SOTA) techniques.

Light field angular super-resolution (LFASR) aims to reconstruct densely sampled angular views from sparsely captured inputs, enabling high-fidelity rendering, refocusing, and depth estimation. In this paper, we propose a novel LFASR framework that employs a tri-visualization feature extraction strategy, which jointly processes Sub-Aperture Images (SAIs), Epipolar Plane Images (EPIs), and Macro-Pixel Images (MacroPIs) to comprehensively exploit the spatial-angular structure of light fields. These complementary representations are processed in parallel to extract diverse and informative features, which are then refined through a deep spatial aggregation module composed of residual blocks. The proposed pipeline consists of three key stages: Early Feature Extraction (EFE), Advanced Feature Refinement (AFR), and Angular Super-Resolution (ASR). Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves strong performance in terms of PSNR and SSIM, while maintaining strong generalization and robustness in challenging scenarios, including occlusions and large disparity variations. Moreover, qualitative evaluations confirm the method’s ability to preserve epipolar consistency and structural integrity across synthesized views, making it a reliable and efficient solution for practical LFASR applications.

Light field angular super-resolution (LFASR) aims to reconstruct densely sampled views from sparse inputs by exploiting spatial–angular correlations, thereby producing rich spatial–angular representations and enabling applications such as 3D reconstruction, refocusing, and virtual reality. In this paper, we propose a multi-receptive field spatial–angular (MRF-SA) framework that jointly captures fine-grained details and long-range dependencies through complementary spatial and angular branches. This design enables effective modeling of disparity-aware interactions without relying on computationally expensive attention mechanisms. In addition, we introduce a lightweight variant based on depth-wise separable convolutions to achieve a favorable tradeoff between reconstruction accuracy and computational efficiency. Extensive experiments on both real-world and synthetic datasets demonstrate that the proposed method achieves competitive performance compared to state-of-the-art approaches.

Light field angular super-resolution (LFASR) seeks to recover missing viewpoints from sparsely captured angular samples, while preserving the underlying scene structure across views. This task is complicated by disparity-induced variations, incomplete visibility near occlusion boundaries, and view-dependent appearance changes caused by reflective surfaces. In this work, we propose a cross-domain EPI–spatial–angular (X-ESA) framework that combines EPI-based geometric modeling with complementary spatial and angular feature extraction. A CSWin-based EPI transformer captures long-range, slope-aligned dependencies, while dedicated convolutional branches refine spatial details and cross-view relationships. These features are integrated through a mesh–progressive fusion strategy, followed by channel attention and spatial residual refinement. Experiments on real-world and synthetic datasets under the $2\times 2\to 7\times 7$ setting result in average performances of $41.29$ dB/$0.989$ SSIM and $40.98$ dB/$0.989$ SSIM, respectively. The proposed model contains $2.84$ M trainable parameters and provides competitive reconstruction quality relative to existing LFASR methods.