Improving the noise immunity of 3D computational ghost imaging

Feature ghost imaging for color identification

On the basis of computational ghost imaging (CGI), we present a new imaging technique, feature ghost imaging (FGI), which can convert the color information into distinguishable edge features in retrieved grayscale images. With the edge features extracted by different order operators, FGI can obtain the shape and the color information of objects simultaneously in a single-round detection using one single-pixel detector. The feature distinction of rainbow colors is presented in numerical simulations and the verification of FGI's practical performance is conducted in experiments. Furnishing a new perspective to the imaging of colored objects, our FGI extends the function and the application fields of traditional CGI while sustaining the simplicity of the experimental setup.

Three-dimensional computational ghost imaging using a dynamic virtual projection unit generated by Risley prisms

Computational ghost imaging (CGI) using stereo vision is able to achieve three-dimensional (3D) imaging by using multiple projection units or multiple bucket detectors which are separated spatially. We present a compact 3D CGI system that consists of Risley prisms, a stationary projection unit and a bucket detector. By rotating double prisms to various angles, speckle patterns appear to be projected by a dynamic virtual projection unit at different positions and multi-view ghost images are obtained for 3D imaging. In the process of reconstruction, a convolutional neural network (CNN) for super-resolution (SR) is adopted to enhance the angular resolution of reconstructed images. Moreover, an optimized 3D CNN is implemented for disparity estimation and 3D reconstruction. The experimental results validate the effectiveness of the method and indicate that the compact system with flexibility has potential in applications such as navigation and detection.

3D Fourier ghost imaging via semi-calibrated photometric stereo

We achieved three-dimensional (3D) computational ghost imaging with multiple photoresistors serving as single-pixel detectors using the semi-calibrated lighting approach. We performed imaging in the spatial frequency domain by having each photoresistor obtain the Fourier spectrum of the scene at a low spectral coverage ratio of 5%. To retrieve a depth map of a scene, we inverted, apodized, and applied semi-calibrated photometric stereo (SCPS) to the spectra. At least 93.5% accuracy was achieved for the 3D results of the apodized set of images applied with SCPS in comparison with the ground truth. Furthermore, intensity error map statistics obtained at least 97.0% accuracy for the estimated surface normals using our method. Our system does not need special calibration objects or any additional optical components to perform accurate 3D imaging, making it easily adaptable. Our method can be applied in current imaging systems where multiple detectors operating at any wavelength are used for two-dimensional (2D) imaging, such as imaging cosmological objects. Employing the idea of changing light patterns to illuminate a target scene and having stored information about these patterns, the data retrieved by one detector will give the 2D information while the multiple-detector system can be used to get a 3D profile.

Adaptive compressed 3D ghost imaging based on the variation of surface normals

Three-dimensional (3D) imaging can be reconstructed by a computational ghost imaging system with single pixel detectors based on a photometric stereo, but the requirement of large measurements and long imaging times are obstacles to its development. Also, the compressibility of the target's surface normals has not been fully studied, which causes the waste in sampling efficiency in single-pixel imaging. In this paper, we propose a method to adaptively measure the object's 3D information based on surface normals. In the proposed method, the regions of object's surface are illuminated by patterns of different spatial resolutions according to the variation of surface normals. The experimental results demonstrate that our proposed scheme can reduce measurements and preserve the quality of the formed 3D image.