This paper aims to reconstruct the object-side light field from the focal stack focusing on different imaging planes. In the forward problem, the focal stack was modeled as projections of the light field. Based on projection modeling, both the filtered-backprojection(FBP) method and the Landweber iterative scheme of solving the inverse problem regarding light field reconstruction from focal stack were derived by applying the methods of computerized tomography(CT). The experimental results show that the high-precision light field can be reconstructed via FBP and Simultaneous-Algebraic-Reconstruction-Technique(SART) algorithm, and depth and surface of the scene can be reconstructed via the reconstructed light field.

Depth reconstruction from the light field, as a depth extracting approach, is a vibrant research field in computational imaging. In this paper, depth reconstruction from the light field was approached as an optimization problem by analyzing the forward and inverse models. The forward and inverse models present the connection between the light field and the depth of the scene. We proposed an iterative method for scene depth reconstruction with fidelity from 4D light field data. The objective function of the optimization problem entails three terms, of which the matching term is used as the fidelity term, while the gradient term and classification term constitute the penalty terms. The iterative method minimized the objective function by correcting mismatching pixels iteratively. In this way, the reconstruction accuracy of the scene depth was significantly improved with high robustness, especially in the weak texture and occlusion regions. The experimental results show that the depth map can be reconstructed with high accuracy by the iterative method, in alignment with high depth resolution in the weak texture and occlusion regions. An accurate 3D surface in the field of view can be achieved using the depth information reconstructed from the 4D light field.

Tiny target detection in marine scenes is of practical importance in marine vision applications such as personnel search and rescue, navigation safety, and marine management. In the past few years, methods based on deep convolutional neural networks (CNN) have performed well for targets of common sizes. However, the accurate detection of tiny targets in marine scene images is affected by three difficulties: perspective multiscale, tiny target pixel ratios, and complex backgrounds. We proposed the feature pyramid network model based on multiscale attention to address the problem of tiny target detection in aerial beach images with large field-of-view, which forms the basis for the tiny target recognition and counting. To improve the ability of the tiny targets’ feature extraction, the proposed model focuses on different scales of the images to the target regions based on the multiscale attention enhancement module. To improve the effectiveness of tiny targets’ feature fusion, the pyramid structure is guided by the feature fusion module in order to give further semantic information to the low-level feature maps and prevent the tiny targets from being overwhelmed by the information at the high-level. Experimental results show that the proposed model generally outperforms existing models, improves accuracy by 8.56 percent compared to the baseline model, and achieves significant performance gains on the TinyPerson dataset. The code is publicly available via Github.

Background: How it is possible to “faithfully” represent a three-dimensional stereoscopic scene using Cartesian coordinates on a plane, and how three-dimensional perceptions differ between an actual scene and an image of the same scene are questions that have not yet been explored in depth. They seem like commonplace phenomena, but in fact, they are important and difficult issues for visual information processing, neural computation, physics, psychology, cognitive psychology, and neuroscience.Results: The results of this study show that the use of plenoptic (or all-optical) functions and their dual plane parameterizations can not only explain the nature of information processing from the retina to the primary visual cortex and, in particular, the characteristics of the visual pathway’s optical system and its affine transformation, but they can also clarify the reason why the vanishing point and line exist in a visual image. In addition, they can better explain the reasons why a three-dimensional Cartesian coordinate system can be introduced into the two-dimensional plane to express a real three-dimensional scene.Conclusions: 1. We introduce two different mathematical expressions of the plenoptic functions,P_wandP_vthat can describe the objective world. We also analyze the differences between these two functions when describing visual depth perception, that is, the difference between how these two functions obtain the depth information of an external scene.2. The main results include a basic method for introducing a three-dimensional Cartesian coordinate system into a two-dimensional plane to express the depth of a scene, its constraints, and algorithmic implementation. In particular, we include a method to separate the plenoptic function and proceed with the corresponding transformation in the retina and visual cortex.3. We propose that size constancy, the vanishing point, and vanishing line form the basis of visual perception of the outside world, and that the introduction of a three-dimensional Cartesian coordinate system into a two dimensional plane reveals a corresponding mapping between a retinal image and the vanishing point and line.

In this paper, we propose the symmetric structure of the reconstructed points discretization model to partition and order the subsets of Ordered Subset Expectation Maximum (OSEM) algorithms for image reconstruction and then simplify the calculation of the projection coefficient matrix while satisfying the balancing properties of subsets. The reconstructed points discretization model was utilized to describe the forward and inverse relationships between the reconstructed points and the projection data according to the distance from the point to the ray rather than the intersection length between the square pixel and the ray. This discretization model provides new approaches for improving and constructing the reconstruction algorithms on the basis of the geometry of the model. The experimental results show that the OSEM algorithms based on the reconstructed points discretization model and its geometric symmetry structure can effectively improve the imaging speed and the imaging precision.