Traditional hyperspectral imagers rely on scanning either the spectral or spatial dimension of the hyperspectral cube with spectral filters or line-scanning which can be time consuming and generally require precise moving parts, increasing the complexity. More recently, snapshot techniques have emerged, enabling capture of the full hyperspectral datacube in a single shot. However, some types of these snapshot system are bulky and complicated, which is difficult to apply to the real world. Therefore, this paper proposes a compact snapshot hyperspectral imaging system based on compressive theory, which consists of the imaging lens, light splitter, micro lens array, a metasurface-covered sensor and an RGB camera. The light of the object first passes through the imaging lens, and then a splitter divides the light equally into two directions. The light in one direction pass through the microlens array and then the light modulation is achieved by using a metasurface on the imaging sensor. Meanwhile, the light in another direction is received directly by an RGB camera. This system has the following advantages: first, the metasurface supercell can be well designed and arranged to optimize the transfer matrix of the system; second, the microlens array guarantee that the light incident on the metasurface at a small angle, which eliminate the transmittance error introduced by the incidence angle; third, the RGB camera is able to provide side information and help to ease the reconstruction.
Due to the low cost and easy deployment, the depth estimation of monocular cameras has always attracted attention of researchers. As good performance based on deep learning technology in depth estimation, more and more training models has emerged for depth estimation. Most existing works have required very promising results that belongs to supervised learning methods, but corresponding ground truth depth data for training is inevitable that makes training complicated. To overcome this limitation, an unsupervised learning framework is used for monocular depth estimation from videos, which contains depth map and pose network. In this paper, better results can be achieved by optimizing training models and improving training loss. Besides, training and evaluation data is based on standard dataset KITTI (Karlsruhe Institute of Technology and Toyota Institute of Technology). In the end, the results are shown through comparing with different training models used in this paper.
Spectral confocal technology is an important three-dimensional measurement technology with high accuracy and non-contact; however, traditional spectral confocal system usually consists of prisons and several lens whose volume and weight is enormous and heavy, besides, due to the chromatic aberration characteristics of ordinary optical lenses, it is difficult to perfectly focus light in a wide bandwidth. Meta-surfaces are expected to realize the miniaturization of conventional optical element due to its superb abilities of controlling phase and amplitude of wavefront of incident at subwavelength scale, and in this paper, an efficient spectral confocal meta-lens (ESCM) working in the near infrared spectrum (1300nm-2000nm) is proposed and numerically demonstrated. ESCM can focus incident light at different focal lengths from 16.7 to 24.5μm along a perpendicular off-axis focal plane with NA varying from 0.385 to 0.530. The meta-lens consists of a group of Si nanofins providing high polarization conversion efficiency lager than 50%, and the phase required for focusing incident light is well rebuilt by the resonant phase which is proportional to the frequency and the wavelength-independent geometric phase, PB phase. Such dispersive components can also be used in implements requiring dispersive device such as spectrometers.
A novel method is proposed in this paper for light field depth estimation by using a convolutional neural network. Many approaches have been proposed to make light field depth estimation, while most of them have a contradiction between accuracy and runtime. In order to solve this problem, we proposed a method which can get more accurate light field depth estimation results with faster speed. First, the light field data is augmented by proposed method considering the light field geometry. Because of the large amount of the light field data, the number of images needs to be reduced appropriately to improve the operation speed, while maintaining the confidence of the estimation. Next, light field images are inputted into our network after data augmentation. The features of the images are extracted during the process, which could be used to calculate the disparity value. Finally, our network can generate an accurate depth map from the input light field image after training. Using this accurate depth map, the 3D structure in real world could be accurately reconstructed. Our method is verified by the HCI 4D Light Field Benchmark and real-world light field images captured with a Lytro light field camera.
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