Sang Chin

High-speed flow microscopy using compressed sensing with ultrafast laser pulses.

We demonstrate an imaging system employing continuous high-rate photonically-enabled compressed sensing (CHiRP-CS) to enable efficient microscopic imaging of rapidly moving objects with only a few percent of the samples traditionally required for Nyquist sampling. Ultrahigh-rate spectral shaping is achieved through chirp processing of broadband laser pulses and permits ultrafast structured illumination of the object flow. Image reconstructions of high-speed microscopic flows are demonstrated at effective rates up to 39.6 Gigapixel/sec from a 720-MHz sampling rate.

Compact all-CMOS spatiotemporal compressive sensing video camera with pixel-wise coded exposure

We present a low power all-CMOS implementation of temporal compressive sensing with pixel-wise coded exposure. This image sensor can increase video pixel resolution and frame rate simultaneously while reducing data readout speed. Compared to previous architectures, this system modulates pixel exposure at the individual photo-diode electronically without external optical components. Thus, the system provides reduction in size and power compare to previous optics based implementations. The prototype image sensor (127 × 90 pixels) can reconstruct 100 fps videos from coded images sampled at 5 fps. With 20× reduction in readout speed, our CMOS image sensor only consumes 14μW to provide 100 fps videos.

A dictionary learning algorithm for multi-channel neural recordings

Multi-channel neural recording devices are widely used for in vivo neuroscience experiments. Incurred by high signal frequency and large channel numbers, the acquisition rate could be on the order of hundred MB/s, which requires compression before wireless transmission. In this paper, we adopt the Compressed Sensing framework with a simple on-chip implementation. To improve the performance while reducing the number of measurements, we propose a multi-modal structured dictionary learning algorithm that enforces both group sparsity and joint sparsity to learn sparsifying dictionaries for all channels simultaneously. When the data is compressed 50 times, our method can achieve a gain of 4 dB and 10 percentage units over state-of-art approaches in terms of the reconstruction quality and classification accuracy, respectively.

Ultrawideband compressed sensing of arbitrary multi-tone sparse radio frequencies using spectrally encoded ultrafast laser pulses

We demonstrate a photonic system for pseudorandom sampling of multi-tone sparse radio-frequency (RF) signals in an 11.95-GHz bandwidth using <1% of the measurements required for Nyquist sampling. Pseudorandom binary sequence (PRBS) patterns are modulated onto highly chirped laser pulses, encoding the patterns onto the optical spectra. The pulses are partially compressed to increase the effective sampling rate by 2.07×, modulated with the RF signal, and fully compressed yielding optical integration of the PRBS-RF inner product prior to photodetection. This yields a 266× reduction in the required electronic sampling rate. We introduce a joint-sparsity-based matching-pursuit reconstruction via bagging to achieve accurate recovery of tones at arbitrary frequencies relative to the reconstruction basis.

Energy-efficient two-stage Compressed Sensing method for implantable neural recordings

For in-vivo neuroscience experiments, implantable neural recording devices have been widely used to capture neural activity. With high acquisition rate, these devices require efficient on-chip compression methods to reduce power consumption for the subsequent wireless transmission. Recently, Compressed Sensing (CS) approaches have shown great potentials, but there exists the tradeoff between the complexity of the sensing circuit and its compression performance. To address this challenge, we proposed a two-stage CS method, including an on-chip sensing using random Bernoulli Matrix S and an off-chip sensing using Puffer transformation P. Our approach allows a simple circuit design and improves the reconstruction performance with the off-chip sensing. Moreover, we proposed to use measureed data as the sparsifying dictionary D. It delivers comparable reconstruction performance to the signal dependent dictionary and outperforms the standard basis. It also allows both D and P to be updated incrementally with reduced complexity. Experiments on simulation and real datasets show that the proposed approach can yield an average SNDR gain of more than 2 dB over other CS approaches.

72 MHz A-scan optical coherence tomography using continuous high-rate photonically-enabled compressed sensing (CHiRP-CS)

Randomly spectrally patterned laser pulses acquire more information in each sample, allowing for increasing imaging speed independent of detector limitations.

Compressive high speed flow microscopy with motion contrast(Conference Presentation)

High-speed continuous imaging systems are constrained by analog-to-digital conversion, storage, and transmission. However, real video signals of objects such as microscopic cells and particles require only a few percent or less of the full video bandwidth for high fidelity representation by modern compression algorithms. Compressed Sensing (CS) is a recent influential paradigm in signal processing that builds real-time compression into the acquisition step by computing inner products between the signal of interest and known random waveforms and then applying a nonlinear reconstruction algorithm. Here, we extend the continuous high-rate photonically-enabled compressed sensing (CHiRP-CS) framework to acquire motion contrast video of microscopic flowing objects. We employ chirp processing in optical fiber and high-speed electro-optic modulation to produce ultrashort pulses each with a unique pseudorandom binary sequence (PRBS) spectral pattern with 325 features per pulse at the full laser repetition rate (90 MHz). These PRBS-patterned pulses serve as random structured illumination inside a one-dimensional (1D) spatial disperser. By multiplexing the PRBS patterns with a user-defined repetition period, the difference signal y_i=phi_i (x_i - x_{i-tau}) can be computed optically with balanced detection, where x is the image signal, phi_i is the PRBS pattern, and tau is the repetition period of the patterns. Two-dimensional (2D) image reconstruction via iterative alternating minimization to find the best locally-sparse representation yields an image of the edges in the flow direction, corresponding to the spatial and temporal 1D derivative. This provides both a favorable representation for image segmentation and a sparser representation for many objects that can improve image compression.

Compressed sensing block-wise exposure control algorithm using optical flow estimation

Recently, CMOS image sensors have attracted more and more attention from the applications of navigation, monitoring and search-and-rescue operations. Specially, CMOS image sensors mounted on insects need to be fast, adaptive to the environment and power efficiency. To simultaneously satisfy both requirements of reconstruction quality and low power consumption, we propose a compressed sensing block-wise exposure control algorithm using optical flow estimation. This framework has been demonstrated to further improve recovery performance (> 25 dB) with high compression ratio (>= 10 : 1), which also provides a promising method for real-time CMOS implementation.

Compressive ultrahigh-speed continuous imaging using spectrally structured ultrafast laser pulses

We demonstrate an ultrahigh-rate imaging system applied to very high speed microscopic flows. Chirp processing of ultrafast laser pulses in optical fiber is employed to create pseudorandom spectral patterns at a rate of one unique pattern per pulse. These spectral patterns then serve as structured illumination of the object flows inside a 1D spatial disperser before digitization at a rate of one sample per optical pulse with a fast single pixel photodetector. Diffraction-limited microscopic imaging of flows up to 31.2 m/s is achieved at up to 19.8 and 39.6 Gigapixel/sec rates from a 720 MHz acquisition rate.