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Computational temporal ghost imaging (CTGI) allows the reconstruction of a fast signal from a two dimensional detection with no temporal resolution. High speed spatial modulation is implemented to encode temporal detail of the signal into the two dimensional detection. By calculating the correlation between the modulation and the rendered image, the temporal information can be retrieved. CTGI indicates a way to detect high speed non-reproducible signal from a slow detector. Based on CTGI, we propose an innovative scheme that can increase the frame rate of a camera by resolving the temporal detail of every camera image. To achieve this, CTGI is conducted parallelly to different areas of the scene. High speed spatial multiplexed modulation is performed, constraining the continuous scene into a series of short-time-scale frames. All the modulated frames are accumulated into one image that is eventually used in the correlation retrieval process. By performing CTGI reconstruction on each area independently, the temporal detail of the whole scene can be obtained. This method can have a strong application in ultrafast imaging.
A new focal-plane three-dimensional (3D) imaging method based on temporal ghost imaging is proposed and demonstrated. By exploiting the advantages of temporal ghost imaging, this method enables slow integrating cameras have an ability of 3D surface i
Based on optical correlations, ghost imaging is usually reconstructed by computer algorithm from the acquired data. We here proposed an alternatively high contrast naked-eye ghost imaging scheme which avoids computer algorithm processing. Instead, th
The image information acquisition ability of a conventional camera is usually much lower than the Shannon Limit since it does not make use of the correlation between pixels of image data. Applying a random phase modulator to code the spectral images
Ghost imaging LiDAR via sparsity constraints using push-broom scanning is proposed. It can image the stationary target scene continuously along the scanning direction by taking advantage of the relative movement between the platform and the target sc
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained mer