Compressive Holographic Video

Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate $10\times$ temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.Comment: 12 pages, 6 figure

Simultaneous Bayesian Compressive Sensing and Blind Deconvolution

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Video compressive sensing with on-chip programmable subsampling

The maximum achievable frame-rate for a video camera is limited by the sensor’s pixel readout rate. The same sen-sor may achieve either a slow frame-rate at full resolution (e.g., 60 fps at 4 Mpixel resolution) or a fast frame-rate at low resolution (e.g., 240 fps at 1Mpixel resolution). Higher frame-rates are achieved using pixel readout modes (e.g., subsampling or binning) that sacrifice spatial for temporal resolution within a fixed bandwidth. A number of compres-sive video cameras have been introduced to overcome this fixed bandwidth constraint and achieve high frame-rates without sacrificing spatial resolution. These methods use electro-optic components (e.g., LCoS, DLPs, piezo actua-tors) to introduce high speed spatio-temporal multiplexing in captured images. Full resolution, high speed video is then restored by solving an undetermined system of equa-tions using a sparse regularization framework. In this work, we introduce the first all-digital temporal compressive video camera that uses custom subsampling modes to achieve spatio-temporal multiplexing. Unlike previous compressive video cameras, ours requires no additional optical compo-nents, enabling it to be implemented in a compact package such as a mobile camera module. We demonstrate results using a TrueSense development kit with a 12 Mpixel sensor and programmable FPGA read out circuitry. 1

Panchromatic Diffraction Gratings for Miniature Computationally Efficient Visual-bar-position Sensing

We describe the design and performance of an ultraminiature lensless computational sensor optimized for estimating the one-dimensional position of visual bars. The sensor consists of a special-purpose wavelength-robust optical binary phase diffraction grating affixed to a CMOS photodetector array. This grating does not produce a traditional high-quality human interpretable image on the photodetectors, but instead yields visual information relevant to the bar-position estimation problem. Computationally efficient algorithms then process this sensed information to yield an accurate estimate of the position of the bar. The optical grating is very small (120 µm diameter), has large angle of view (140 0), and extremely large depth of field (0.5 mm to infinity). The design of this sensor demonstrates the power of end-to-end optimization (optics and digital processing) for high accuracy and very low computational cost in a new class of ultraminiature computational sensors

Automated Eardrum Registration From Light-Field Data

The performance of automated classification algorithms for medical images needs to be very high and especially robust in order to be adopted into healthcare. Most of the time the main challenge is unregistered data, since it is usually captured: 1) from different patients, 2) with different devices, and 3) at different time. Registration and normalization of the captured data is a necessary condition for success. In this paper we present for the first time an automated method to register eardrums from light-field data. This procedure uses the shape information captured by a light-field otoscope and compensates for the natural tilt of the eardrum, its size, and the camera viewpoint. Results on clinical data show that the proposed algorithm is robust and works well for different types of ear conditions