1,617 research outputs found
Off-the-grid model based deep learning (O-MODL)
We introduce a model based off-the-grid image reconstruction algorithm using
deep learned priors. The main difference of the proposed scheme with current
deep learning strategies is the learning of non-linear annihilation relations
in Fourier space. We rely on a model based framework, which allows us to use a
significantly smaller deep network, compared to direct approaches that also
learn how to invert the forward model. Preliminary comparisons against image
domain MoDL approach demonstrates the potential of the off-the-grid
formulation. The main benefit of the proposed scheme compared to structured
low-rank methods is the quite significant reduction in computational
complexity.Comment: ISBI 201
Convolutional Dictionary Learning: Acceleration and Convergence
Convolutional dictionary learning (CDL or sparsifying CDL) has many
applications in image processing and computer vision. There has been growing
interest in developing efficient algorithms for CDL, mostly relying on the
augmented Lagrangian (AL) method or the variant alternating direction method of
multipliers (ADMM). When their parameters are properly tuned, AL methods have
shown fast convergence in CDL. However, the parameter tuning process is not
trivial due to its data dependence and, in practice, the convergence of AL
methods depends on the AL parameters for nonconvex CDL problems. To moderate
these problems, this paper proposes a new practically feasible and convergent
Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
BPG-M-based CDL is investigated with different block updating schemes and
majorization matrix designs, and further accelerated by incorporating some
momentum coefficient formulas and restarting techniques. All of the methods
investigated incorporate a boundary artifacts removal (or, more generally,
sampling) operator in the learning model. Numerical experiments show that,
without needing any parameter tuning process, the proposed BPG-M approach
converges more stably to desirable solutions of lower objective values than the
existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
Compared to the ADMM approaches, the BPG-M method using a multi-block updating
scheme is particularly useful in single-threaded CDL algorithm handling large
datasets, due to its lower memory requirement and no polynomial computational
complexity. Image denoising experiments show that, for relatively strong
additive white Gaussian noise, the filters learned by BPG-M-based CDL
outperform those trained by the ADMM approach.Comment: 21 pages, 7 figures, submitted to IEEE Transactions on Image
Processin
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
The most efficient signal edge-preserving smoothing filters, e.g., for
denoising, are non-linear. Thus, their acceleration is challenging and is often
performed in practice by tuning filter parameters, such as by increasing the
width of the local smoothing neighborhood, resulting in more aggressive
smoothing of a single sweep at the cost of increased edge blurring. We propose
an alternative technology, accelerating the original filters without tuning, by
running them through a special conjugate gradient method, not affecting their
quality. The filter non-linearity is dealt with by careful freezing and
restarting. Our initial numerical experiments on toy one-dimensional signals
demonstrate 20x acceleration of the classical bilateral filter and 3-5x
acceleration of the recently developed guided filter.Comment: 5 pages, 5 figures, IEEE Conference GlobalSIP 201
Edge-enhancing Filters with Negative Weights
In [DOI:10.1109/ICMEW.2014.6890711], a graph-based denoising is performed by
projecting the noisy image to a lower dimensional Krylov subspace of the graph
Laplacian, constructed using nonnegative weights determined by distances
between image data corresponding to image pixels. We~extend the construction of
the graph Laplacian to the case, where some graph weights can be negative.
Removing the positivity constraint provides a more accurate inference of a
graph model behind the data, and thus can improve quality of filters for
graph-based signal processing, e.g., denoising, compared to the standard
construction, without affecting the costs.Comment: 5 pages; 6 figures. Accepted to IEEE GlobalSIP 2015 conferenc
Burst Denoising with Kernel Prediction Networks
We present a technique for jointly denoising bursts of images taken from a
handheld camera. In particular, we propose a convolutional neural network
architecture for predicting spatially varying kernels that can both align and
denoise frames, a synthetic data generation approach based on a realistic noise
formation model, and an optimization guided by an annealed loss function to
avoid undesirable local minima. Our model matches or outperforms the
state-of-the-art across a wide range of noise levels on both real and synthetic
data.Comment: To appear in CVPR 2018 (spotlight). Project page:
http://people.eecs.berkeley.edu/~bmild/kpn
Signal reconstruction via operator guiding
Signal reconstruction from a sample using an orthogonal projector onto a
guiding subspace is theoretically well justified, but may be difficult to
practically implement. We propose more general guiding operators, which
increase signal components in the guiding subspace relative to those in a
complementary subspace, e.g., iterative low-pass edge-preserving filters for
super-resolution of images. Two examples of super-resolution illustrate our
technology: a no-flash RGB photo guided using a high resolution flash RGB
photo, and a depth image guided using a high resolution RGB photo.Comment: 5 pages, 8 figures. To appear in Proceedings of SampTA 2017: Sampling
Theory and Applications, 12th International Conference, July 3-7, 2017,
Tallinn, Estoni
Convolutional Sparse Coding with Overlapping Group Norms
The most widely used form of convolutional sparse coding uses an
regularization term. While this approach has been successful in a variety of
applications, a limitation of the penalty is that it is homogeneous
across the spatial and filter index dimensions of the sparse representation
array, so that sparsity cannot be separately controlled across these
dimensions. The present paper considers the consequences of replacing the
penalty with a mixed group norm, motivated by recent theoretical
results for convolutional sparse representations. Algorithms are developed for
solving the resulting problems, which are quite challenging, and the impact on
the performance of the denoising problem is evaluated. The mixed group norms
are found to perform very poorly in this application. While their performance
is greatly improved by introducing a weighting strategy, such a strategy also
improves the performance obtained from the much simpler and computationally
cheaper norm
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images
Edge preserving filters preserve the edges and its information while blurring
an image. In other words they are used to smooth an image, while reducing the
edge blurring effects across the edge like halos, phantom etc. They are
nonlinear in nature. Examples are bilateral filter, anisotropic diffusion
filter, guided filter, trilateral filter etc. Hence these family of filters are
very useful in reducing the noise in an image making it very demanding in
computer vision and computational photography applications like denoising,
video abstraction, demosaicing, optical-flow estimation, stereo matching, tone
mapping, style transfer, relighting etc. This paper provides a concrete
introduction to edge preserving filters starting from the heat diffusion
equation in olden to recent eras, an overview of its numerous applications, as
well as mathematical analysis, various efficient and optimized ways of
implementation and their interrelationships, keeping focus on preserving the
boundaries, spikes and canyons in presence of noise. Furthermore it provides a
realistic notion for efficient implementation with a research scope for
hardware realization for further acceleration.Comment: Manuscrip
Convolutional Sparse Representations with Gradient Penalties
While convolutional sparse representations enjoy a number of useful
properties, they have received limited attention for image reconstruction
problems. The present paper compares the performance of block-based and
convolutional sparse representations in the removal of Gaussian white noise.
While the usual formulation of the convolutional sparse coding problem is
slightly inferior to the block-based representations in this problem, the
performance of the convolutional form can be boosted beyond that of the
block-based form by the inclusion of suitable penalties on the gradients of the
coefficient maps
Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification
The cryo-electron microscope (cryo-EM) is increasingly popular these years.
It helps to uncover the biological structures and functions of macromolecules.
In this paper, we address image denoising problem in cryo-EM. Denoising the
cryo-EM images can help to distinguish different molecular conformations and
improve three dimensional reconstruction resolution. We introduce the use of
data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF
algorithm is closely related to the dictionary learning. The advantage of DDTF
algorithm is that it is computationally efficient, and can well identify the
texture and shape of images without using large data samples. Experimental
results on cryo-EM image denoising and conformational classification
demonstrate the power of DDTF algorithm for cryo-EM image denoising and
classification.Comment: 2018 IEEE Global Signal and Information Processin
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