Photon-Limited Imaging
Low-Light Demosaicking and Denoising for Small Pixels Using Learned Frequency Selection
IEEE Transactions on Computational Imaging, 2021
Omar Elgendy, Abhiram Gnanasambandam, Stanley H. Chan, and Jiaju Ma
Manuscript: https://ieeexplore.ieee.org/abstract/document/9335264
Low-light imaging is a challenging task because of the excessive photon shot noise. Color imaging in low-light is even more difficult because one needs to demosaick and denoise simultaneously. Existing demosaicking algorithms are mostly designed for well-illuminated scenarios, which fail to work with low-light. Recognizing the recent development of small pixels and low read noise image sensors, we propose a learning-based joint demosaicking and denoising algorithm for low-light color imaging. Our method combines the classical theory of color filter arrays and modern deep learning. We use an explicit carrier to demodulate the color from the input Bayer pattern image. We integrate trainable filters into the demodulation scheme to improve flexibility. We introduce a guided filtering module to transfer knowledge from the luma channel to the chroma channels, thus offering substantially more reliable denoising. Extensive experiments are performed to evaluate the performance of the proposed method, using both synthetic datasets and real data. Results indicate that the proposed method offers consistently better performance over the current state-of-the-art, across several standard evaluation metrics.
HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction
IEEE Transactions on Computational Imaging, 2020
Abhiram Gnanasambandam and Stanley H. Chan
Manuscript: https://arxiv.org/abs/2011.03614
Code: https://github.itap.purdue.edu/StanleyChanGroup/QIS_HDR_TCI20
An earlier version of the paper is presented at International Image Sensor Workshop (IISW) 2019.
Manuscript: PDF
High dynamic range (HDR) imaging is one of the biggest achievements in modern
photography. Traditional solutions to HDR imaging are designed for and
applied to CMOS image sensors (CIS). However, the mainstream one-micron CIS
cameras today generally have a high read noise and low frame-rate. These, in
turn, limit the acquisition speed and quality, making the cameras slow in the
HDR mode. In this paper, we propose a new computational photography technique
for HDR imaging. Recognizing the limitations of CIS, we use the Quanta Image
Sensor (QIS) to trade the spatial-temporal resolution with bit-depth. QIS is
a single-photon image sensor that has comparable pixel pitch to CIS but
substantially lower dark current and read noise. We provide a complete
theoretical characterization of the sensor in the context of HDR imaging, by
proving the fundamental limits in the dynamic range that QIS can offer and
the trade-offs with noise and speed. In addition, we derive an optimal
reconstruction algorithm for single-bit and multi-bit QIS. Our algorithm is
theoretically optimal for emph{all} linear reconstruction schemes based on
exposure bracketing. Experimental results confirm the validity of the theory
and algorithm, based on synthetic and real QIS data.
Dynamic Low-light Imaging with Quanta Image Sensors
European Conference on Computer Vision (ECCV), 2020.
Manuscript: https://arxiv.org/abs/2007.08614
Yiheng Chi, Abhiram Gnanasambandam, Vladlen Koltun, and Stanley H. Chan
Code: https://github.itap.purdue.edu/StanleyChanGroup/ECCV2020_Dynamic
Imaging in low light is difficult because the number of photons arriving at
the sensor is low. Imaging dynamic scenes in low-light environments is even
more difficult because as the scene moves, pixels in adjacent frames need to
be aligned before they can be denoised. Conventional CMOS image sensors (CIS)
are at a particular disadvantage in dynamic low-light settings because the
exposure cannot be too short lest the read noise overwhelms the signal. We
propose a solution using Quanta Image Sensors (QIS) and present a new image
reconstruction algorithm. QIS are single-photon image sensors with photon
counting capabilities. Studies over the past decade have confirmed the
effectiveness of QIS for low-light imaging but reconstruction algorithms for
dynamic scenes in low light remain an open problem. We fill the gap by
proposing a student-teacher training protocol that transfers knowledge from a
motion teacher and a denoising teacher to a student network. We show that
dynamic scenes can be reconstructed from a burst of frames at a photon level
of 1 photon per pixel per frame. Experimental results confirm the advantages
of the proposed method compared to existing methods.
Image Classification in the Dark using Quanta Image Sensors
European Conference on Computer Vision (ECCV), 2020
Manuscript: https://arxiv.org/abs/2006.02026
Abhiram Gnanasambandam, and Stanley H. Chan
Code: https://github.itap.purdue.edu/StanleyChanGroup/QIS_ImageClassification_ECCV20
Abstract:
State-of-the-art image classifiers are trained and tested using
well-illuminated images. These images are typically captured by CMOS image
sensors with at least tens of photons per pixel. However, in dark
environments when the photon flux is low, image classification becomes
difficult because the measured signal is suppressed by noise. In this paper,
we present a new low-light image classification solution using Quanta Image
Sensors (QIS). QIS are a new type of image sensors that possess photon
counting ability without compromising on pixel size and spatial resolution.
Numerous studies over the past decade have demonstrated the feasibility of
QIS for low-light imaging, but their usage for image classification has not
been studied. This paper fills the gap by presenting a student-teacher
learning scheme which allows us to classify the noisy QIS raw data. We show
that with student-teacher learning, we are able to achieve image
classification at a photon level of one photon per pixel or lower.
Experimental results verify the effectiveness of the proposed method compared
to existing solutions.
Color Filter Arrays Design for Quanta Image Sensors
IEEE Trans. Computational Imaging, 2019.
Manuscript: https://arxiv.org/abs/1903.09823
Omar A. Elgendy, and Stanley H. Chan
Quanta image sensor (QIS) is envisioned to be the next generation image
sensor after CCD and CMOS. In this paper, we discuss how to design color
filter arrays for QIS. Designing color filter arrays for small pixels such as
QIS is challenging because maximizing the light efficiency while suppressing
aliasing and crosstalk are conflicting tasks. We present an
optimization-based framework which unifies several mainstream color filter
array design methodologies and offers greater generality and flexibility.
Compared to existing methods, the new framework can simultaneously handle
luminance sensitivity, chrominance sensitivity, cross-talk, anti-aliasing,
manufacturability and orthogonality. Extensive experimental comparisons
demonstrate the effectiveness and generality of the framework.
Megapixel photon-counting color imaging using quanta image sensor
OSA Optics Express 2019
Manuscript: https://arxiv.org/abs/1903.09036
Abhiram Gnanasambandam, Omar A. Elgendy, Jiaju Ma, and Stanley H. Chan
Code: https://github.itap.purdue.edu/StanleyChanGroup/ColorRecon_OpEx19
Quanta Image Sensor (QIS) is a single-photon detector designed for extremely
low light imaging conditions. Majority of the existing QIS prototypes are
monochrome based on single-photon avalanche diodes (SPAD). Passive color
imaging has not been demonstrated with single-photon detectors due to the
intrinsic difficulty of shrinking the pixel size and increasing the spatial
resolution while maintaining acceptable intra-pixel cross-talk. In this
paper, we present image reconstruction of the first color QIS with a
resolution of 1024-by-1024 pixels, supporting both single-bit and multi-bit
photon counting capability. Our color image reconstruction is enabled by a
customized joint demosaicing-denoising algorithm, leveraging truncated
Poisson statistics and variance stabilizing transforms. Experimental results
of the new sensor and algorithm demonstrate superior color imaging
performance for very low-light conditions with a mean exposure of as low as a
few photons per pixel in both real and simulated images.
Photon-counting Imaging with Multi-bit Quanta Image Sensors
International Image Sensor Workshop (IISW), 2019
Manuscript: PDF
Jiaju Ma, Yu-Wing Chung, Abhiram Gnanasambandam, Stanley H. Chan, and Saleh Masoodian
Image Reconstruction for Quanta Image Sensors using Deep Neural Networks
IEEE ICASSP, 2019
Manuscript: PDF
Joon Hee Choi, Omar A. Elgendy and Stanley H. Chan
Quanta Image Sensor (QIS) is a single-photon image sensor that oversamples
the light field to generate binary measurements. Its single-photon
sensitivity makes it an ideal candidate for the next generation image sensor
after CMOS. However, image reconstruction of the sensor remains a challenging
issue. Existing image reconstruction algorithms are largely based on
optimization. In this paper, we present the first deep neural network
approach for QIS image reconstruction. Our deep neural network takes the
binary bit stream of QIS as input, learns the nonlinear transformation and
denoising simultaneously. Experimental results show that the proposed network
produces significantly better reconstruction results compared to existing
methods.
Optimal Threshold Design for Quanta Image Sensors
(ICIP 2016 Best Paper Award)
IEEE Trans. Computational Imaging, 2018
Manuscript: https://arxiv.org/abs/1704.03886
Omar A. Elgendy and Stanley H. Chan
Also presented in IEEE ICIP 2016
Manuscript: PDF
MATLAB Implementation (1.1MB)
Quanta Image Sensor (QIS) has been envisioned as a candidate solution for next generation image sensors. We provide two
new contributions to the signal processing aspects of QIS. First, we develop an image reconstruction algorithm to
recover the underlying images from the QIS data, which is a massive array of binarized Poisson random variables. The
new algorithm supersedes existing methods by enabling arbitrary threshold level. Second, we present a threshold design
scheme to adaptively update the threshold level for optimal image reconstruction. We discuss the existence of a phase
transition in determining the optimal threshold. Experimental results on tone-mapped high dynamic range images
validates the effectiveness of the threshold scheme and the image reconstruction algorithm.
Non-Iterative Image Reconstruction
 |
Quanta image sensor (QIS) is a class of single photon imaging devices that measure light intensity
using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is
a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these
bits. In this paper, we present a non-iterative image reconstruction algorithm for QIS. Unlike existing reconstruction
methods that formulate the problem from an optimization perspective, the new algorithm directly recovers the images
through a pair of nonlinear transformations and an off-the-shelf image denoising algorithm. By skipping the usual
optimization procedure, we achieve orders of magnitude improvement in speed and even better image reconstruction
quality. We validate the new algorithm on synthetic datasets as well as real videos collected by 1-bit SPAD cameras. Publication:
Code: https://github.itap.purdue.edu/StanleyChanGroup/SingleBitRecon_MDPI_16 |
ADMM Image Reconstruction
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Recent advances in materials, devices and fabrication technologies have motivated a strong momentum in developing
solid-state sensors that can detect individual photons in space and time. It has been envisioned that such sensors can
eventually achieve very high spatial resolutions as well as high frame rates. In this paper, we present an efficient
algorithm to reconstruct images from the massive binary bit-streams generated by these sensors. Based on the concept of
alternating direction method of multipliers (ADMM), we transform the computationally intensive optimization problem
into a sequence of subproblems, each of which has efficient implementations in the form of polyphase-domain filtering
or pixel-wise nonlinear mappings. Moreover, we reformulate the original maximum likelihood estimation as maximum a
posterior estimation by introducing a total variation prior. Numerical results demonstrate the strong performance of
the proposed method, which achieves several dB's of improvement in PSNR and requires a shorter runtime as compared to
standard gradient-based approaches. Publication:
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