Bio: Ce Sun is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Filter bank. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.
TL;DR: An adaptive retina-like sampling model (ARSM) is proposed to balance autofocusing accuracy and efficiency and results show that the performances of the method are better than that of the traditional method.
TL;DR: In this paper , a new fusion framework based on Quaternion Non-Subsampled Contourlet Transform (QNSCT) and Guided Filter detail enhancement is designed to address the problems of inconspicuous infrared targets and poor background texture in Infrared and visible image fusion.
TL;DR: Wang et al. as mentioned in this paper proposed a four-beam sparse phase retrieval (F-BSPR) algorithm, which uses the phase differences from both horizontal and vertical components and the phase difference from other components when the phase is retrieving.
Abstract: Sheared-beam imaging (SBI) is an effective way of imaging through turbulent medium, such as atmosphere or scattering liquid. Traditionally, the imaging is based on the laser transmitter array consisting of three beams or five beams for coherent illumination to the remote object. Compared with the existing SBI methods, the four-beam sparse sampling imaging method has been proposed, which may have more advantages; it not only sparses the detector elements but also reduces the number of emitted beams. However, the traditional phase retrieval algorithms are not suitable for the four-beam sparse sampling imaging. We propose a four-beam sparse phase retrieval (F-BSPR) algorithm, which uses the phase differences from both horizontal and vertical components and the phase differences from other components when the phase is retrieving. The proposed phase retrieval algorithm can better connect the phase difference and improve the accuracy of the phase retrieval. Furthermore, the imaging quality is improved. Simulation and experimental results show that the proposed algorithm is effective and feasible when the number of detector elements is sparse by 50%. Compared to the traditional four-beam phase retrieval method, the proposed F-BSPR method has better imaging quality and robustness.
TL;DR: A spatially adaptive retina-like sampling method for 3-D imaging Lidar based on time-of-flight method is proposed, which demonstrates that the proposed method is capable of decreasing data acquisition time without considerable distortion of the interested target.
Abstract: To mitigate the conflict between imaging quality and speed, a spatially adaptive retina-like sampling method for 3-D imaging Lidar based on time-of-flight method is proposed. The differences between previous retina-like sampling method and the proposed method are described. Sampling points with dense distribution is for the area of interest while sparse distribution is for the area of uninterest, which obtains high imaging quality while consuming much less data acquisition time. Mathematical models of the spatially adaptive retina-like method are developed, and the key parameters are analyzed. To validate the spatially adaptive retina-like sampling method, we perform situational simulations to compare the proposed method with the previous one. Results demonstrate that the proposed method is capable of decreasing data acquisition time without considerable distortion of the interested target. Furthermore, the proposed method is analyzed under different scenes for single and multiple targets. Results illustrate that the proposed method performs better than the previous method.
TL;DR: An improved autofocus method for human red blood cell images in a microscope using the properties of a Gaussian function and an adaptive focus window with great robustness is proposed that can reduce the computation cost and adverse effects of the background.
Abstract: This paper presents an improved autofocus method for human red blood cell images in a microscope. The products of the sum modulus difference and the real-valued fast Fourier transform function are multiplied to obtain an improved sharpness evaluation using the properties of a Gaussian function. It is superior to traditional evaluations in terms of unimodality, steepness, and sensitivity. A new quantitative criterion is proposed to represent the ability of sharpness evaluation against noise. An adaptive focus window with great robustness is proposed that can reduce the computation cost and adverse effects of the background. The better performances of the proposed algorithms are all proved by experiment results, and they can help to find the quasi-focus position more quickly and accurately.
TL;DR: In this article , an autofocusing algorithm using pixel difference with the Tanimoto coefficient (PDTC) is described to predict the focus, which can robustly distinguish differences in clarity among datasets.
Abstract: Focusing objects accurately over short time scales is an essential and nontrivial task for a variety of microscopy applications. In this Letter, an autofocusing algorithm using pixel difference with the Tanimoto coefficient (PDTC) is described to predict the focus. Our method can robustly distinguish differences in clarity among datasets. The generated auto-focusing curves have extremely high sensitivity. A dataset of a defocused stack acquired by an Olympus microscope demonstrates the feasibility of our technique. This work can be applied in full-color microscopic imaging systems and is also valid for single-color imaging.
TL;DR: A deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here.
Abstract: In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.