As densely-sampled Light Field (LF) images are beneficial to many applications, LF reconstruction becomes an important technology in related fields. Recently, neural rendering shows great potential in reconstruction tasks. However, volume rendering in existing methods needs to sample many points on the whole camera ray or epipolar line, which is time-consuming. In this paper, specifically for LF images with regular angular sampling, we propose a novel Structure-Aware Pre-Selected neural rendering framework for LF reconstruction. Instead of sampling on the whole epipolar line, we propose to sample on several specific positions, which are estimated using the color and inherent scene structure information explored in the regular angular sampled LF images. Sampling only a few points that closely match the target pixel, the feature of the target pixel is quickly rendered with high-quality. Finally, we fuse the features and decode them in the view dimension to obtain the final target view. Experiments show that the proposed method outperforms the state-of-the-art LF reconstruction methods in both qualitative and quantitative comparisons across various tasks. Our method also surpasses the most existing methods in terms of speed. Moreover, without any retraining or fine-tuning, the performance of our method with no-per-scene optimization is even better than the methods with per-scene optimization.

Recently, Light Field (LF) shows great potential in removing occlusion since the objects occluded in some views may be visible in other views. However, existing LF-based methods implicitly model each scene and can only remove objects that have positive disparities in one central views. In this article, we propose a novel Progressive Multi-Plane Images (MPI) Construction method specifically designed for LF-based occlusion removal. Different from the previous MPI construction methods, we progressively construct MPIs layer by layer in order from near to far. In order to accurately model the current layer, the positions of foreground occlusions in the nearer layers are taken as occlusion prior. Specifically, we propose an Occlusion-Aware Attention Network to generate each layer of MPIs with reliable information in occluded regions. For each layer, occlusions in the current layer are filtered out so that the background is better recovered just using the visible views instead of the other occluded views. Then, by simply removing the layers containing occlusions and rendering MPIs in kinds of viewpoints, the occlusion removal results for different views are generated. Experiments on synthetic and real-world scenes show that our method outperforms state-of-the-art LF occlusion removal methods in quantitative and visual comparisons. Moreover, we also apply the proposed progressive MPI construction method to the view synthesis task. The occlusion edges in our synthesized views achieve significantly better quality, which also verifies that our method can better model the occluded regions.

In the hybrid lenses Light Field (LF) images, a high-resolution (HR) camera is in the center of the multiple low-resolution (LR) cameras, which introduces the beneficial high-frequency information for LF super-resolution. Therefore, how to effectively utilize the high-frequency information of the central view is the key issue for the hybrid lenses LF images super-resolution. In this paper, we propose a novel learning-based framework with Layered Refinement to super-resolve the hybrid lenses LF images. Specifically, we first transform the depth information of the scene into the layered position information, and refine it by complementing the high-frequency information of the HR central view to generate a high-quality representation of the depth information. Then, guided by high-quality depth representation, we propagate the information of the HR central view to the surrounding views accurately, and utilize the layered position information to maintain the occlusion relationship during the propagation. Moreover, as the generation of each layer position information is independent in our method, our trained model can flexibly adapt kinds of scenes with various disparity ranges without additional training. Experiments show that the proposed method outperforms the SOTA methods in kinds of scenes from simulated and real-world datasets with various disparity ranges. The code is available at \urlhttps://github.com/racso10/LFHSR.

Light Field (LF) cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of LF cameras. In this paper, an end-to-end learning-based method is proposed to simultaneously reconstruct all view images in LFs with higher spatial resolution. Based on the epipolar geometry, view images in one LF are first grouped into several image stacks and fed into different network branches to learn sub-pixel details for each view image. Since LFs have dense sampling in angular domain, sub-pixel details in multiple spatial directions are learned from corresponding angular directions in multiple branches, respectively. Then, sub-pixel details from different directions are further integrated to generate global high-frequency residual details. Combined with the spatially upsampled LF, the final LF with high spatial resolution is obtained. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods in both visual and numerical evaluations. We also implement the proposed method on LFs with different angular resolution and experiments show that the proposed method achieves superior results than others, especially for LFs with small angular resolution. Furthermore, since the epipolar geometry is fully considered, the proposed network shows good performances in preserving the inherent epipolar property in LF images.

This paper proposes a nine-bridge single-stage isolated three-phase bidirectional AC/DC converter, which realizes buck-boost voltage regulation, high-frequency electrical isolation, and bidirectional power transmission. The converter adopts three transformers to distribute the total power equally, and the number of switches is significantly reduced by optimizing the topology. The Y-Δ connection of the transformer can provide an additional step-down conversion ratio. The improved Space Vector Pulse Width Modulation (SVPWM) algorithm is proposed to guarantee the voltage-second balance of the transformers and unity power factor control of the AC side. The proper selection of the switching states of the zero voltage vector contributes to an increase in the operating frequency of the transformers, which is 1.5 times that of the switches connected with them. The operating principles of the converter are presented in detail as well as its SVPWM algorithm. The simulation results under the rectifier mode and the inverter mode in MATLAB/Simulink validate the theoretical analysis well.