993 research outputs found
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Advanced Restoration Techniques for Images and Disparity Maps
With increasing popularity of digital cameras, the field of Computa-
tional Photography emerges as one of the most demanding areas of
research. In this thesis we study and develop novel priors and op-
timization techniques to solve inverse problems, including disparity
estimation and image restoration.
The disparity map estimation method proposed in this thesis incor-
porates multiple frames of a stereo video sequence to ensure temporal
coherency. To enforce smoothness, we use spatio-temporal connec-
tions between the pixels of the disparity map to constrain our solution.
Apart from smoothness, we enforce a consistency constraint for the
disparity assignments by using connections between the left and right
views. These constraints are then formulated in a graphical model,
which we solve using mean-field approximation. We use a filter-based
mean-field optimization that perform efficiently by updating the dis-
parity variables in parallel. The parallel updates scheme, however, is
not guaranteed to converge to a stationary point. To compare and
demonstrate the effectiveness of our approach, we developed a new
optimization technique that uses sequential updates, which runs ef-
ficiently and guarantees convergence. Our empirical results indicate
that with proper initialization, we can employ the parallel update
scheme and efficiently optimize our disparity maps without loss of
quality. Our method ranks amongst the state of the art in common
benchmarks, and significantly reduces the temporal flickering artifacts
in the disparity maps.
In the second part of this thesis, we address several image restora-
tion problems such as image deblurring, demosaicing and super-
resolution. We propose to use denoising autoencoders to learn an
approximation of the true natural image distribution. We parametrize
our denoisers using deep neural networks and show that they learn
the gradient of the smoothed density of natural images. Based on
this analysis, we propose a restoration technique that moves the so-
lution towards the local extrema of this distribution by minimizing
the difference between the input and output of our denoiser. Weii
demonstrate the effectiveness of our approach using a single trained
neural network in several restoration tasks such as deblurring and
super-resolution. In a more general framework, we define a new
Bayes formulation for the restoration problem, which leads to a more
efficient and robust estimator. The proposed framework achieves state
of the art performance in various restoration tasks such as deblurring
and demosaicing, and also for more challenging tasks such as noise-
and kernel-blind image deblurring.
Keywords. disparity map estimation, stereo matching, mean-field
optimization, graphical models, image processing, linear inverse prob-
lems, image restoration, image deblurring, image denoising, single
image super-resolution, image demosaicing, deep neural networks,
denoising autoencoder
Linear Reconstruction of Non-Stationary Image Ensembles Incorporating Blur and Noise Models
Two new linear reconstruction techniques are developed to improve the resolution of images collected by ground-based telescopes imaging through atmospheric turbulence. The classical approach involves the application of constrained least squares (CLS) to the deconvolution from wavefront sensing (DWFS) technique. The new algorithm incorporates blur and noise models to select the appropriate regularization constant automatically. In all cases examined, the Newton-Raphson minimization converged to a solution in less than 10 iterations. The non-iterative Bayesian approach involves the development of a new vector Wiener filter which is optimal with respect to mean square error (MSE) for a non-stationary object class degraded by atmospheric turbulence and measurement noise. This research involves the first extension of the Wiener filter to account properly for shot noise and an unknown, random optical transfer function (OTF). The vector Wiener filter provides superior reconstructions when compared to the traditional scalar Wiener filter for a non-stationary object class. In addition, the new filter can provide a superresolution capability when the object\u27s Fourier domain statistics are known for spatial frequencies beyond the OTF cutoff. A generalized performance and robustness study of the vector Wiener filter showed that MSE performance is fundamentally limited by object signal-to-noise ratio (SNR) and correlation between object pixels
Region-Based Approach for Single Image Super-Resolution
Single image super-resolution (SR) is a technique that generates a high- resolution image from a single low-resolution image [1,2,10,11]. Single image super- resolution can be generally classified into two groups: example-based and self-similarity based SR algorithms. The performance of the example-based SR algorithm depends on the similarity between testing data and the database. Usually, a large database is needed for better performance in general. This would result in heavy computational cost. The self-similarity based SR algorithm can generate a high-resolution (HR) image with sharper edges and fewer ringing artifacts if there is sufficient recurrence within or across scales of the same image [10, 11], but it is hard to generate HR details for an image region with fine texture.
Based on the limitation of each type of SR algorithm, we propose to combine these two types of algorithms. We segment each image into regions based on image content, and choose the appropriate SR algorithm to recover the HR image for each region based on the texture feature. Our experimental results show that our proposed method takes advantage of each SR algorithm and can produce natural looking results with sharp edges, while suppressing ringing artifacts. We compute PSNR to qualitatively evaluate the SR results, and our proposed method outperforms the self-similarity based or example-based SR algorithm with higher PSNR (+0.1dB)
Level Set KSVD
We present a new algorithm for image segmentation - Level-set KSVD. Level-set
KSVD merges the methods of sparse dictionary learning for feature extraction
and variational level-set method for image segmentation. Specifically, we use a
generalization of the Chan-Vese functional with features learned by KSVD. The
motivation for this model is agriculture based. Aerial images are taken in
order to detect the spread of fungi in various crops. Our model is tested on
such images of cotton fields. The results are compared to other methods.Comment: 25 pages, 14 figures. Submitted to IJC
PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction
Metal artifacts caused by the presence of metallic implants tremendously
degrade the reconstructed computed tomography (CT) image quality, affecting
clinical diagnosis or reducing the accuracy of organ delineation and dose
calculation in radiotherapy. Recently, deep learning methods in sinogram and
image domains have been rapidly applied on metal artifact reduction (MAR) task.
The supervised dual-domain methods perform well on synthesized data, while
unsupervised methods with unpaired data are more generalized on clinical data.
However, most existing methods intend to restore the corrupted sinogram within
metal trace, which essentially remove beam hardening artifacts but ignore other
components of metal artifacts, such as scatter, non-linear partial volume
effect and noise. In this paper, we mathematically derive a physical property
of metal artifacts which is verified via Monte Carlo (MC) simulation and
propose a novel physics based non-local dual-domain network (PND-Net) for MAR
in CT imaging. Specifically, we design a novel non-local sinogram decomposition
network (NSD-Net) to acquire the weighted artifact component, and an image
restoration network (IR-Net) is proposed to reduce the residual and secondary
artifacts in the image domain. To facilitate the generalization and robustness
of our method on clinical CT images, we employ a trainable fusion network
(F-Net) in the artifact synthesis path to achieve unpaired learning.
Furthermore, we design an internal consistency loss to ensure the integrity of
anatomical structures in the image domain, and introduce the linear
interpolation sinogram as prior knowledge to guide sinogram decomposition.
Extensive experiments on simulation and clinical data demonstrate that our
method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure
A machine learning framework to optimize optic nerve electrical stimulation for vision restoration
Optic nerve electrical stimulation is a promising technique to restore vision in blind subjects. Machine learning methods can be used to select effective stimulation protocols, but they require a model of the stimulated system to generate enough training data. Here, we use a convolutional neural network (CNN) as a model of the ventral visual stream. A genetic algorithm drives the activation of the units in a layer of the CNN representing a cortical region toward a desired pattern, by refining the activation imposed at a layer representing the optic nerve. To simulate the pattern of activation elicited by the sites of an electrode array, a simple point-source model was introduced and its optimization process was investigated for static and dynamic scenes. Psychophysical data confirm that our stimulation evolution framework produces results compatible with natural vision. Machine learning approaches could become a very powerful tool to optimize and personalize neuroprosthetic systems
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