Light field imaging for computer vision:a survey

Light field(LF)imaging has attracted attention because of its ability to solve computer vision problems.In this paper we briefly review the research progress in computer vision in recent years.For most factors that affect computer vision development,the richness and accuracy of visual information acquisition are decisive.LF imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays,acquiring complete three-dimensional(3D)scene information.LF imaging technology improves the accuracy of depth estimation,image segmentation,blending,fusion,and 3D reconstruction.LF has also been innovatively applied to iris and face recognition,identification of materials and fake pedestrians,acquisition of epipolar plane images,shape recovery,and LF microscopy.Here,we further summarize the existing problems and the development trends of LF imaging in computer vision,including the establishment and evaluation of the LF dataset,applications under high dynamic range(HDR)conditions,LF image enhancement,virtual reality,3D display,and 3D movies,military optical camouflage technology,image recognition at micro-scale,image processing method based on HDR,and the optimal relationship between spatial resolution and four-dimensional(4D)LF information acquisition.LF imaging has achieved great success in various studies.Over the past 25 years,more than 180 publications have reported the capability of LF imaging in solving computer vision problems.We summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).

Design and fabrication of silicon-tessellated structures for monocentric imagers

Compared with conventional planar optical image sensors,a curved focal plane array can simplify the lens design and improve the field of view.In this paper,we introduce the design and implementation of a segmented,hemispherical,CMOS-compatible silicon image plane for a 10-mm diameter spherical monocentric lens.To conform to the hemispherical focal plane of the lens,we use flexible gores that consist of arrays of spring-connected silicon hexagons.Mechanical functionality is demonstrated by assembling the 20-μm-thick silicon gores into a hemispherical test fixture.We have also fabricated and tested a photodiode array on a siliconon-insulator substrate for use with the curved imager.Optical testing shows that the fabricated photodiodes achieve good performance;the hemispherical imager enables a compact 160°field of view camera with >80% fill factor using a single spherical lens.