2,682 research outputs found
A unified joint reconstruction approach in structured illumination microscopy using unknown speckle patterns
The structured illumination microscopy using unknown speckle patterns has
shown the capacity to surpass the Abbe's diffraction barrier, giving the
possibility to design cheap and versatile SIM devices. However, the
state-of-the-art joint reconstruction methods in this framework has a
relatively low contrast in super-resolution part in comparison to conventional
SIM and the hyper-parameter is not easy to tune. In this paper, a unified joint
reconstruction approach is proposed with the hyper-parameter proportional to
the noise level. Different regularization terms could be evaluated under the
same model. Moreover, the degradation entailed by out-of-focus light could be
solved in speckle illumination setup easily.Comment: 11 page
A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising
Due to the factors like processing power limitations and channel capabilities
images are often down sampled and transmitted at low bit rates resulting in a
low resolution compressed image. High resolution images can be reconstructed
from several blurred, noisy and down sampled low resolution images using a
computational process know as super resolution reconstruction. Super-resolution
is the process of combining multiple aliased low-quality images to produce a
high resolution, high-quality image. The problem of recovering a high
resolution image progressively from a sequence of low resolution compressed
images is considered. In this paper we propose a novel DCT based progressive
image display algorithm by stressing on the encoding and decoding process. At
the encoder we consider a set of low resolution images which are corrupted by
additive white Gaussian noise and motion blur. The low resolution images are
compressed using 8 by 8 blocks DCT and noise is filtered using our proposed
novel zonal filter. Multiframe fusion is performed in order to obtain a single
noise free image. At the decoder the image is reconstructed progressively by
transmitting the coarser image first followed by the detail image. And finally
a super resolution image is reconstructed by applying our proposed novel
adaptive interpolation technique. We have performed both objective and
subjective analysis of the reconstructed image, and the resultant image has
better super resolution factor, and a higher ISNR and PSNR. A comparative study
done with Iterative Back Projection (IBP) and Projection on to Convex Sets
(POCS),Papoulis Grechberg, FFT based Super resolution Reconstruction shows that
our method has out performed the previous contributions.Comment: 20 pages, 11 figure
Image Reconstruction with Predictive Filter Flow
We propose a simple, interpretable framework for solving a wide range of
image reconstruction problems such as denoising and deconvolution. Given a
corrupted input image, the model synthesizes a spatially varying linear filter
which, when applied to the input image, reconstructs the desired output. The
model parameters are learned using supervised or self-supervised training. We
test this model on three tasks: non-uniform motion blur removal,
lossy-compression artifact reduction and single image super resolution. We
demonstrate that our model substantially outperforms state-of-the-art methods
on all these tasks and is significantly faster than optimization-based
approaches to deconvolution. Unlike models that directly predict output pixel
values, the predicted filter flow is controllable and interpretable, which we
demonstrate by visualizing the space of predicted filters for different tasks.Comment: https://www.ics.uci.edu/~skong2/pff.htm
Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
Deconvolution microscopy has been extensively used to improve the resolution
of the widefield fluorescent microscopy. Conventional approaches, which usually
require the point spread function (PSF) measurement or blind estimation, are
however computationally expensive. Recently, CNN based approaches have been
explored as a fast and high performance alternative. In this paper, we present
a novel unsupervised deep neural network for blind deconvolution based on cycle
consistency and PSF modeling layers. In contrast to the recent CNN approaches
for similar problem, the explicit PSF modeling layers improve the robustness of
the algorithm. Experimental results confirm the efficacy of the algorithm
Motion Deblurring for Plenoptic Images
We address for the first time the issue of motion blur in light field images
captured from plenoptic cameras. We propose a solution to the estimation of a
sharp high resolution scene radiance given a blurry light field image, when the
motion blur point spread function is unknown, i.e., the so-called blind
deconvolution problem. In a plenoptic camera, the spatial sampling in each view
is not only decimated but also defocused. Consequently, current blind
deconvolution approaches for traditional cameras are not applicable. Due to the
complexity of the imaging model, we investigate first the case of uniform
(shift-invariant) blur of Lambertian objects, i.e., when objects are
sufficiently far away from the camera to be approximately invariant to depth
changes and their reflectance does not vary with the viewing direction. We
introduce a highly parallelizable model for light field motion blur that is
computationally and memory efficient. We then adapt a regularized blind
deconvolution approach to our model and demonstrate its performance on both
synthetic and real light field data. Our method handles practical issues in
real cameras such as radial distortion correction and alignment within an
energy minimization framework
Robust Statistics for Image Deconvolution
We present a blind multiframe image-deconvolution method based on robust
statistics. The usual shortcomings of iterative optimization of the likelihood
function are alleviated by minimizing the M-scale of the residuals, which
achieves more uniform convergence across the image. We focus on the
deconvolution of astronomical images, which are among the most challenging due
to their huge dynamic ranges and the frequent presence of large noise-dominated
regions in the images. We show that high-quality image reconstruction is
possible even in super-resolution and without the use of traditional
regularization terms. Using a robust \r{ho}-function is straightforward to
implement in a streaming setting and, hence our method is applicable to the
large volumes of astronomy images. The power of our method is demonstrated on
observations from the Sloan Digital Sky Survey (Stripe 82) and we briefly
discuss the feasibility of a pipeline based on Graphical Processing Units for
the next generation of telescope surveys
Fast Single Image Super-Resolution
This paper addresses the problem of single image super-resolution (SR), which
consists of recovering a high resolution image from its blurred, decimated and
noisy version. The existing algorithms for single image SR use different
strategies to handle the decimation and blurring operators. In addition to the
traditional first-order gradient methods, recent techniques investigate
splitting-based methods dividing the SR problem into up-sampling and
deconvolution steps that can be easily solved. Instead of following this
splitting strategy, we propose to deal with the decimation and blurring
operators simultaneously by taking advantage of their particular properties in
the frequency domain, leading to a new fast SR approach. Specifically, an
analytical solution can be obtained and implemented efficiently for the
Gaussian prior or any other regularization that can be formulated into an
-regularized quadratic model, i.e., an - optimization
problem. Furthermore, the flexibility of the proposed SR scheme is shown
through the use of various priors/regularizations, ranging from generic image
priors to learning-based approaches. In the case of non-Gaussian priors, we
show how the analytical solution derived from the Gaussian case can be embedded
intotraditional splitting frameworks, allowing the computation cost of existing
algorithms to be decreased significantly. Simulation results conducted on
several images with different priors illustrate the effectiveness of our fast
SR approach compared with the existing techniques
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
Image restoration, including image denoising, super resolution, inpainting,
and so on, is a well-studied problem in computer vision and image processing,
as well as a test bed for low-level image modeling algorithms. In this work, we
propose a very deep fully convolutional auto-encoder network for image
restoration, which is a encoding-decoding framework with symmetric
convolutional-deconvolutional layers. In other words, the network is composed
of multiple layers of convolution and de-convolution operators, learning
end-to-end mappings from corrupted images to the original ones. The
convolutional layers capture the abstraction of image contents while
eliminating corruptions. Deconvolutional layers have the capability to upsample
the feature maps and recover the image details. To deal with the problem that
deeper networks tend to be more difficult to train, we propose to symmetrically
link convolutional and deconvolutional layers with skip-layer connections, with
which the training converges much faster and attains better results.Comment: 17 pages. A journal extension of the version at arXiv:1603.0905
Randomized Aperture Imaging
Speckled images of a binary broad band light source (600-670 nm), generated
by randomized reflections or transmissions, were used to reconstruct a binary
image by use of multi-frame blind deconvolution algorithms. Craft store glitter
was used as reflective elements. Another experiment used perforated foil. Also
reported here are numerical models that afforded controlled tip-tilt and piston
aberrations. These results suggest the potential importance of a poorly
figured, randomly varying segmented imaging system.Comment: 10 pages, 9 figures, draft for OSA journa
Image Restoration using Autoencoding Priors
We propose to leverage denoising autoencoder networks as priors to address
image restoration problems. We build on the key observation that the output of
an optimal denoising autoencoder is a local mean of the true data density, and
the autoencoder error (the difference between the output and input of the
trained autoencoder) is a mean shift vector. We use the magnitude of this mean
shift vector, that is, the distance to the local mean, as the negative log
likelihood of our natural image prior. For image restoration, we maximize the
likelihood using gradient descent by backpropagating the autoencoder error. A
key advantage of our approach is that we do not need to train separate networks
for different image restoration tasks, such as non-blind deconvolution with
different kernels, or super-resolution at different magnification factors. We
demonstrate state of the art results for non-blind deconvolution and
super-resolution using the same autoencoding prior
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