Stephen S. Welsh

3D Computational Imaging with Single-Pixel Detectors

Cheap Pix Three-dimensional (3D) images can be captured by, for example, holographic imaging or stereoimaging techniques. To avoid using expensive optical components that are limited to specialized bands of wavelengths, Sun et al. (p. 844; see the Perspective by Faccio and Leach) projected pulses of randomly textured light onto an object. They were able to reconstruct an image of the 3D object by detecting the reflected light with several photodetectors without any need for lenses. The patterned light beams can thus in principle be substituted for light sources of any wavelength. A computational imaging method is used to reconstruct a three-dimensional scene, without the need for lenses. [Also see Perspective by Faccio and Leach] Computational imaging enables retrieval of the spatial information of an object with the use of single-pixel detectors. By projecting a series of known random patterns and measuring the backscattered intensity, it is possible to reconstruct a two-dimensional (2D) image. We used several single-pixel detectors in different locations to capture the 3D form of an object. From each detector we derived a 2D image that appeared to be illuminated from a different direction, even though only a single digital projector was used for illumination. From the shading of the images, the surface gradients could be derived and the 3D object reconstructed. We compare our result to that obtained from a stereophotogrammetric system using multiple cameras. Our simplified approach to 3D imaging can readily be extended to nonvisible wavebands.

Normalized ghost imaging

We present an experimental comparison between different iterative ghost imaging algorithms. Our experimental setup utilizes a spatial light modulator for generating known random light fields to illuminate a partially-transmissive object. We adapt the weighting factor used in the traditional ghost imaging algorithm to account for changes in the efficiency of the generated light field. We show that our normalized weighting algorithm can match the performance of differential ghost imaging.

Fast full-color computational imaging with single-pixel detectors.

Single-pixel detectors can be used as imaging devices by making use of structured illumination. These systems work by correlating a changing incident light field with signals measured on a photodiode to derive an image of an object. In this work we demonstrate a system that utilizes a digital light projector to illuminate a scene with approximately 1300 different light patterns every second and correlate these with the back scattered light measured by three spectrally-filtered single-pixel photodetectors to produce a full-color high-quality image in a few seconds of data acquisition. We utilize a differential light projection method to self normalize the measured signals, improving the reconstruction quality whilst making the system robust to external sources of noise. This technique can readily be extended for imaging applications at non-visible wavebands.

Simultaneous real-time visible and infrared video with single-pixel detectors.

Conventional cameras rely upon a pixelated sensor to provide spatial resolution. An alternative approach replaces the sensor with a pixelated transmission mask encoded with a series of binary patterns. Combining knowledge of the series of patterns and the associated filtered intensities, measured by single-pixel detectors, allows an image to be deduced through data inversion. In this work we extend the concept of a ‘single-pixel camera’ to provide continuous real-time video at 10 Hz , simultaneously in the visible and short-wave infrared, using an efficient computer algorithm. We demonstrate our camera for imaging through smoke, through a tinted screen, whilst performing compressive sampling and recovering high-resolution detail by arbitrarily controlling the pixel-binning of the masks. We anticipate real-time single-pixel video cameras to have considerable importance where pixelated sensors are limited, allowing for low-cost, non-visible imaging systems in applications such as night-vision, gas sensing and medical diagnostics.

Near video-rate linear Stokes imaging with single-pixel detectors

In this work we demonstrate a polarization sensitive computational imaging system based on a digital micro-mirror device (DMD) and several single-pixel photodetectors. By taking advantage of computational imaging techniques, the light measured by each single-pixel detector can reconstruct a 2D image for a specific linear polarization state. Using the rapid frame-rate of the DMD to continuously project a series of spatially orthogonal illumination patterns, near video-rate reconstructions can be achieved. In addition we extend this approach to provide full-colour images through a process of sequential colour selection (RGB). Taking the difference between photodetector signals from orthogonal linear polarization states, we obtain images corresponding to the linear Stokes parameters. We apply this rapid polarization sensitive imaging system to inert and biological material. Since the spatial information in the images reconstructed by this approach are determined by the projection system, rather than the detectors, the approach provides perfect pixel registration between the various polarization selective images and associated Stokes parameters. Furthermore, the use of single-pixel detectors and the large operational bandwidth afforded by DMDʼs means that the approach can readily be extended for imaging at wavelengths where detector arrays are unavailable or limited.

Multi-wavelength compressive computational ghost imaging

The field of ghost imaging encompasses systems which can retrieve the spatial information of an object through correlated measurements of a projected light field, having spatial resolution, and the associated reflected or transmitted light intensity measured by a photodetector. By employing a digital light projector in a computational ghost imaging system with multiple spectrally filtered photodetectors we obtain high-quality multi-wavelength reconstructions of real macroscopic objects. We compare different reconstruction algorithms and reveal the use of compressive sensing techniques for achieving sub-Nyquist performance. Furthermore, we demonstrate the use of this technology in non-visible and fluorescence imaging applications.

3-Dimensional Computational Ghost Imaging

In this computational ghost imaging system we use a digital light projector to generate structured light patterns and a single-pixel detector to obtain a 2D image of a 3D object. Extending of this technique by using multiple single-pixel detectors in different locations enables a 3D reconstruction.

3D Computational Ghost Imaging

Computational Ghost Imaging uses projection of random patterns and backscattered signals recorded by a photo-diode to give an image. Extending to multiple detectors gives surface gradients from which the object’s 3D form can be reconstructed.