183 research outputs found
Experimental comparison of single-pixel imaging algorithms
Single-pixel imaging (SPI) is a novel technique capturing 2D images using a
photodiode, instead of conventional 2D array sensors. SPI owns high
signal-to-noise ratio, wide spectrum range, low cost, and robustness to light
scattering. Various algorithms have been proposed for SPI reconstruction,
including the linear correlation methods, the alternating projection method
(AP), and the compressive sensing based methods. However, there has been no
comprehensive review discussing respective advantages, which is important for
SPI's further applications and development. In this paper, we reviewed and
compared these algorithms in a unified reconstruction framework. Besides, we
proposed two other SPI algorithms including a conjugate gradient descent based
method (CGD) and a Poisson maximum likelihood based method. Both simulations
and experiments validate the following conclusions: to obtain comparable
reconstruction accuracy, the compressive sensing based total variation
regularization method (TV) requires the least measurements and consumes the
least running time for small-scale reconstruction; the CGD and AP methods run
fastest in large-scale cases; the TV and AP methods are the most robust to
measurement noise. In a word, there are trade-offs between capture efficiency,
computational complexity and robustness to noise among different SPI
algorithms. We have released our source code for non-commercial use
Sampling-based Causal Inference in Cue Combination and its Neural Implementation
Causal inference in cue combination is to decide whether the cues have a
single cause or multiple causes. Although the Bayesian causal inference model
explains the problem of causal inference in cue combination successfully, how
causal inference in cue combination could be implemented by neural circuits, is
unclear. The existing method based on calculating log posterior ratio with
variable elimination has the problem of being unrealistic and task-specific. In
this paper, we take advantages of the special structure of the Bayesian causal
inference model and propose a hierarchical inference algorithm based on
importance sampling. A simple neural circuit is designed to implement the
proposed inference algorithm. Theoretical analyses and experimental results
demonstrate that our algorithm converges to the accurate value as the sample
size goes to infinite. Moreover, the neural circuit we design can be easily
generalized to implement inference for other problems, such as the
multi-stimuli cause inference and the same-different judgment
Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors
Optical coherence tomography (OCT) is an important interferometric diagnostic
technique which provides cross-sectional views of the subsurface microstructure
of biological tissues. However, the imaging quality of high-speed OCT is
limited due to the large speckle noise. To address this problem, this paper
proposes a multi-frame algorithmic method to denoise OCT volume.
Mathematically, we build an optimization model which forces the temporally
registered frames to be low rank, and the gradient in each frame to be sparse,
under logarithmic image formation and noise variance constraints. Besides, a
convex optimization algorithm based on the augmented Lagrangian method is
derived to solve the above model. The results reveal that our approach
outperforms the other methods in terms of both speckle noise suppression and
crucial detail preservation
SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis
Multi-view stereopsis (MVS) tries to recover the 3D model from 2D images. As
the observations become sparser, the significant 3D information loss makes the
MVS problem more challenging. Instead of only focusing on densely sampled
conditions, we investigate sparse-MVS with large baseline angles since the
sparser sensation is more practical and more cost-efficient. By investigating
various observation sparsities, we show that the classical depth-fusion
pipeline becomes powerless for the case with a larger baseline angle that
worsens the photo-consistency check. As another line of the solution, we
present SurfaceNet+, a volumetric method to handle the 'incompleteness' and the
'inaccuracy' problems induced by a very sparse MVS setup. Specifically, the
former problem is handled by a novel volume-wise view selection approach. It
owns superiority in selecting valid views while discarding invalid occluded
views by considering the geometric prior. Furthermore, the latter problem is
handled via a multi-scale strategy that consequently refines the recovered
geometry around the region with the repeating pattern. The experiments
demonstrate the tremendous performance gap between SurfaceNet+ and
state-of-the-art methods in terms of precision and recall. Under the extreme
sparse-MVS settings in two datasets, where existing methods can only return
very few points, SurfaceNet+ still works as well as in the dense MVS setting.
The benchmark and the implementation are publicly available at
https://github.com/mjiUST/SurfaceNet-plus.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI), May 202
Efficient single pixel imaging in Fourier space
Single pixel imaging (SPI) is a novel technique being able to capture 2D
images using a bucket detector with high signal-to-noise ratio, wide spectrum
range and low cost. Conventional SPI projects random illumination patterns to
randomly and uniformly sample the entire scene's information. Determined by the
Nyquist sampling theory, SPI needs either numerous projections or high
computation cost to reconstruct the target scene, especially for
high-resolution cases. To address this issue, we propose an efficient single
pixel imaging technique (eSPI), which instead projects sinusoidal patterns for
importance sampling of the target scene's spatial spectrum in Fourier space.
Specifically, utilizing the centrosymmetric conjugation and sparsity priors of
natural images' spatial spectra, eSPI sequentially projects two
-phase-shifted sinusoidal patterns to obtain each Fourier
coefficient in the most informative spatial frequency bands. eSPI can reduce
requisite patterns by two orders of magnitude compared to conventional SPI,
which helps a lot for fast and high-resolution SPI
Single-shot thermal ghost imaging using wavelength-division multiplexing
Ghost imaging (GI) is a potential imaging technique that reconstructs the
target scene from its correlated measurements with a sequential of patterns.
Restricted by the multi-shot principle, GI usually requires long acquisition
time and is limited in observation of dynamic scenes. To handle this problem,
this paper proposes a single-shot thermal ghost imaging scheme via
wavelength-division multiplexing technique. Specifically, we generate thousands
of patterns simultaneously by modulating a broadband light source with a
wavelength dependent diffuser. These patterns carry the scene's spatial
information and then the correlated measurements are coupled into a
spectrometer for the final reconstruction. This technique accelerates the ghost
imaging speed significantly and promotes the applications in dynamic ghost
imaging.Comment: 10 pages, 4 figure
Self-synchronizing scheme for high speed computational ghost imaging
Computational ghost imaging needs to acquire a large number of correlated
measurements between reference patterns and the scene for reconstruction, so
extremely high acquisition speed is crucial for fast ghost imaging. With the
development of technologies, high frequency illumination and detectors are both
available, but their synchronization needs technique demanding customization
and lacks flexibility for different setup configurations. This letter proposes
a self-synchronization scheme that can eliminate this difficulty by introducing
a high precision synchronization technique and corresponding algorithm. We
physically implement the proposed scheme using a 20kHz spatial light modulator
to generate random binary patterns together with a 100 times faster photodiode
for high speed ghost imaging, and the acquisition frequency is around 14 times
faster than that of state-of-the-arts
DeepHuman: 3D Human Reconstruction from a Single Image
We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities
associated with the surface geometry reconstruction, even for the
reconstruction of invisible areas, we propose and leverage a dense semantic
representation generated from SMPL model as an additional input. One key
feature of our network is that it fuses different scales of image features into
the 3D space through volumetric feature transformation, which helps to recover
accurate surface geometry. The visible surface details are further refined
through a normal refinement network, which can be concatenated with the volume
generation network using our proposed volumetric normal projection layer. We
also contribute THuman, a 3D real-world human model dataset containing about
7000 models. The network is trained using training data generated from the
dataset. Overall, due to the specific design of our network and the diversity
in our dataset, our method enables 3D human model estimation given only a
single image and outperforms state-of-the-art approaches
Fast and High Quality Highlight Removal from A Single Image
Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversity of nature scenes is quite challenging. To handle this problem,
we propose an analytic solution to highlight removal based on an L2
chromaticity definition and corresponding dichromatic model. Specifically, this
paper derives a normalized dichromatic model for the pixels with identical
diffuse color: a unit circle equation of projection coefficients in two
subspaces that are orthogonal to and parallel with the illumination,
respectively. In the former illumination orthogonal subspace, which is
specular-free, we can conduct robust clustering with an explicit criterion to
determine the cluster number adaptively. In the latter illumination parallel
subspace, a property called pure diffuse pixels distribution rule (PDDR) helps
map each specular-influenced pixel to its diffuse component. In terms of
efficiency, the proposed approach involves few complex calculation, and thus
can remove highlight from high resolution images fast. Experiments show that
this method is of superior performance in various challenging cases.Comment: 11 pages, 10 figures, submitted to IEEE TI
Rank Minimization for Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems
where multiple frames are mapped into a single measurement, with video
compressive imaging and hyperspectral compressive imaging as two representative
applications. Though exciting results of high-speed videos and hyperspectral
images have been demonstrated, the poor reconstruction quality precludes SCI
from wide applications.This paper aims to boost the reconstruction quality of
SCI via exploiting the high-dimensional structure in the desired signal. We
build a joint model to integrate the nonlocal self-similarity of
video/hyperspectral frames and the rank minimization approach with the SCI
sensing process. Following this, an alternating minimization algorithm is
developed to solve this non-convex problem. We further investigate the special
structure of the sampling process in SCI to tackle the computational workload
and memory issues in SCI reconstruction. Both simulation and real data
(captured by four different SCI cameras) results demonstrate that our proposed
algorithm leads to significant improvements compared with current
state-of-the-art algorithms. We hope our results will encourage the researchers
and engineers to pursue further in compressive imaging for real applications.Comment: 18 pages, 21 figures, and 2 tables. Code available at
https://github.com/liuyang12/DeSC
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