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Jual Dji Mavic 2 Zoom With Smart Controler Harga Terbaik
Research Center for Hyper-Connected Convergence Technology, School of ICT, Robotics and Mechanical Engineering, Institute of Information and Telecommunication Convergence (IITC), Hankyong National University, 327 Chungang-ro, Anseong 17579, Kyonggi-do, Republic of Korea
In this paper, we propose new three-dimensional (3D) visualization of objects at long distance under photon-starved conditions. In conventional three-dimensional image visualization techniques, the visual quality of three-dimensional images may be degraded because object images at long distances may have low resolution. Thus, in our proposed method, we utilize digital zooming, which can crop and interpolate the region of interest from the image to improve the visual quality of three-dimensional images at long distances. Under photon-starved conditions, three-dimensional images at long distances may not be visualized due to the lack of the number of photons. Photon counting integral imaging can be used to solve this problem, but objects at long distance may still have a small number of photons. In our method, a three-dimensional image can be reconstructed, since photon counting integral imaging with digital zooming is used. In addition, to estimate a more accurate three-dimensional image at long distance under photon-starved conditions, in this paper, multiple observation photon counting integral imaging (i.e., N observation photon counting integral imaging) is used. To show the feasibility of our proposed method, we implement the optical experiments and calculate performance metrics, such as peak sidelobe ratio. Therefore, our method can improve the visualization of three-dimensional objects at long distances under photon-starved conditions.
Three-dimensional (3D) visualization of objects at long distances on photon-starved conditions has been a great challenge in many applications, such as military, astronomy, and observing wild animals. In the military case, a defense or reconnaissance that searches enemies at long distances in the day or night is required. In astronomy, observing stars at billions of light years of distance is a critical problem. In addition, observing wild animals, which are nocturnal and have much wariness, is also needed.
Sme Family Firms And Strategic Digital Transformation: Inverting Dualisms Related To Overconfidence And Centralization
However, it is difficult to visualize the three-dimensional objects, which are located at long distances by conventional imaging methods, since lateral and longitudinal resolutions of the image at long distance may be reduced due to the limitation of optical devices and the image sensor. When a camera takes a picture, the object at long distance in the scene has less pixels than a close one. Therefore, lateral and longitudinal resolutions (i.e., three-dimensional resolution) of the image for objects at long distance are reduced. To visualize three-dimensional objects at long distance, integral imaging [1, 2, 3], which was first proposed by G. Lippmann, can be utilized. It uses two-dimensional (2D) images with different perspectives captured by lenslet array or camera array, where these images are referred to as elemental images. Integral imaging can provide full parallax and continuous viewing points of three-dimensional objects without any viewing glasses and coherent light sources [1, 2, 3, 4, 5, 6, 7, 8]. However, due to the limitation of three-dimensional resolution for three-dimensional objects at long distances, the visual quality of three-dimensional images at long distances may be degraded. In addition, this resolution problem may be critical under photon-starved conditions. Because an image sensor detects less photons, which have the information of an object at a long distance under photon-starved conditions, elemental images may not have the information of the object. That is, the visual quality of three-dimensional images may be more degraded under photon-starved conditions.
To visualize three-dimensional objects under photon-starved conditions, photon counting integral imaging [9, 10, 11] has been proposed. It can make a computational model of a photon detector by statistical distribution, such as Poisson distribution, because photons occur rarely in unit time and space [11]. In addition, for three-dimensional image reconstruction, statistical estimation methods, such as maximum likelihood estimation (MLE) [9, 10, 11] or Bayesian approaches [12, 13, 14], are utilized. However, photon counting integral imaging may not estimate the accurate three-dimensional images for objects at long distances under photon-starved conditions, since object images may have low resolution and an insufficient number of photons. Therefore, to solve these problems, a new three-dimensional image visualization technique is required.
In this paper, we propose three-dimensional digital zooming of integral imaging under photon-starved conditions. It can magnify region of interest (ROI) in elemental images captured by synthetic aperture integral imaging (SAII) [15]. Then, three-dimensional images at long distances can be obtained by volumetric computational reconstruction (VCR) [16, 17, 18, 19, 20, 21, 22, 23] and photon counting integral imaging [9, 10, 11, 12, 13, 14]. Under photon-starved conditions, photons can be detected throughout the scene by computational photon counting imaging, which may cause the degradation of resolution for objects due to lack of the number of photons. However, in our method, photon counting imaging is utilized only in ROI of elemental images to visualize three-dimensional images at long distances. Therefore, more photons can be extracted from the ROI of elemental images. In addition, multiple observations of photon counting imaging is considered in our method, where this method is called “N observation photon counting imaging”, which improves the visual quality of the images under photon-starved conditions, since photons are detected randomly for each observation, and multiple observation can increase the number of samples. Additionally, to estimate more accurate three-dimensional images under photon-starved conditions, statistical estimation methods, such as maximum likelihood estimation (MLE), are used in our method.
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This paper is organized as follows. We describe the basic concept of optical and digital zooming and integral imaging in Section 2. Then, we introduce the computational photon counting method and our proposed method in Section 3. To show the feasibility of our proposed method, we show the experimental results in Section 4. Finally, we make a conclusion with summary in Section 5.
In general, to visualize objects at a long distance, two zooming methods, such as optical and digital zooming, can be applied. In optical zooming, objects at long distances can be magnified by modifying lenses’ positions (i.e., use of zoom lens). This can obtain the best visual quality of the image without any interpolation methods. However, it needs more complicated optical devices and is more expensive. On the other hand, in digital zooming, it is easy to visualize objects at long distances by using various interpolation methods. Thus, it is more cost-effective and simpler. However, the visual quality of the image is worse than the image by optical zooming. Recently, two digital zooming methods have been utilized, being interpolation and deep learning based methods. The interpolation method can magnify the image by digital image processing.
In general interpolation methods, there are “nearest”, “bilinear”, and “bicubic” methods. These methods use near pixels to interpolate a pixel value at a new location [24]. The difference between their methods is the number of near pixels. The “nearest” interpolation method uses the single nearest pixel, the “bilinear” interpolation method utilizes the four nearest pixels and distances for interpolation, and the “bicubic” interpolation method interpolates the new pixel by using 16 pixels, which are weighted. The “nearest” method is faster to process than others, but degrading resolution is a disadvantage. The “bicubic” interpolation method has the best result of interpolation, but speed is slow. On the other hand, both the quality and speed of the “bilinear” method are more appropriate than others. Recently, several novel interpolation methods were used, such as the “Lagrange-Chebyshev” and the “de la Vallée-Poussin” [25, 26]. In addition, a deep learning-based method, which can estimate the magnified image by a neural network [27], has been reported. This deep learning-based method can estimate more detail of the zooming region with high magnification ratio in real-time. However, a huge amount of data and time are required to train the neural network.
Visualization Of Crystallographic Orientation And Twist Angles In Two Dimensional Crystals With An Optical Microscope
Cameras in state-of-the-art smart phones utilize interpolation with deep learning to have the best quality of images at long distances. In this paper, we use the interpolation method for digital zooming of objects at long distance because it is easy and fast for implementation. To visualize three-dimensional objects at long distances, we introduce integral imaging, which will
This paper is organized as follows. We describe the basic concept of optical and digital zooming and integral imaging in Section 2. Then, we introduce the computational photon counting method and our proposed method in Section 3. To show the feasibility of our proposed method, we show the experimental results in Section 4. Finally, we make a conclusion with summary in Section 5.
In general, to visualize objects at a long distance, two zooming methods, such as optical and digital zooming, can be applied. In optical zooming, objects at long distances can be magnified by modifying lenses’ positions (i.e., use of zoom lens). This can obtain the best visual quality of the image without any interpolation methods. However, it needs more complicated optical devices and is more expensive. On the other hand, in digital zooming, it is easy to visualize objects at long distances by using various interpolation methods. Thus, it is more cost-effective and simpler. However, the visual quality of the image is worse than the image by optical zooming. Recently, two digital zooming methods have been utilized, being interpolation and deep learning based methods. The interpolation method can magnify the image by digital image processing.
In general interpolation methods, there are “nearest”, “bilinear”, and “bicubic” methods. These methods use near pixels to interpolate a pixel value at a new location [24]. The difference between their methods is the number of near pixels. The “nearest” interpolation method uses the single nearest pixel, the “bilinear” interpolation method utilizes the four nearest pixels and distances for interpolation, and the “bicubic” interpolation method interpolates the new pixel by using 16 pixels, which are weighted. The “nearest” method is faster to process than others, but degrading resolution is a disadvantage. The “bicubic” interpolation method has the best result of interpolation, but speed is slow. On the other hand, both the quality and speed of the “bilinear” method are more appropriate than others. Recently, several novel interpolation methods were used, such as the “Lagrange-Chebyshev” and the “de la Vallée-Poussin” [25, 26]. In addition, a deep learning-based method, which can estimate the magnified image by a neural network [27], has been reported. This deep learning-based method can estimate more detail of the zooming region with high magnification ratio in real-time. However, a huge amount of data and time are required to train the neural network.
Visualization Of Crystallographic Orientation And Twist Angles In Two Dimensional Crystals With An Optical Microscope
Cameras in state-of-the-art smart phones utilize interpolation with deep learning to have the best quality of images at long distances. In this paper, we use the interpolation method for digital zooming of objects at long distance because it is easy and fast for implementation. To visualize three-dimensional objects at long distances, we introduce integral imaging, which will
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