1,130 research outputs found
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
From Symmetry to Geometry: Tractable Nonconvex Problems
As science and engineering have become increasingly data-driven, the role of
optimization has expanded to touch almost every stage of the data analysis
pipeline, from the signal and data acquisition to modeling and prediction. The
optimization problems encountered in practice are often nonconvex. While
challenges vary from problem to problem, one common source of nonconvexity is
nonlinearity in the data or measurement model. Nonlinear models often exhibit
symmetries, creating complicated, nonconvex objective landscapes, with multiple
equivalent solutions. Nevertheless, simple methods (e.g., gradient descent)
often perform surprisingly well in practice.
The goal of this survey is to highlight a class of tractable nonconvex
problems, which can be understood through the lens of symmetries. These
problems exhibit a characteristic geometric structure: local minimizers are
symmetric copies of a single "ground truth" solution, while other critical
points occur at balanced superpositions of symmetric copies of the ground
truth, and exhibit negative curvature in directions that break the symmetry.
This structure enables efficient methods to obtain global minimizers. We
discuss examples of this phenomenon arising from a wide range of problems in
imaging, signal processing, and data analysis. We highlight the key role of
symmetry in shaping the objective landscape and discuss the different roles of
rotational and discrete symmetries. This area is rich with observed phenomena
and open problems; we close by highlighting directions for future research.Comment: review paper submitted to SIAM Review, 34 pages, 10 figure
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
Deep learning techniques have been successfully used in learning a common
representation for multi-view data, wherein the different modalities are
projected onto a common subspace. In a broader perspective, the techniques used
to investigate common representation learning falls under the categories of
canonical correlation-based approaches and autoencoder based approaches. In
this paper, we investigate the performance of deep autoencoder based methods on
multi-view data. We propose a novel step-based correlation multi-modal CNN
(CorrMCNN) which reconstructs one view of the data given the other while
increasing the interaction between the representations at each hidden layer or
every intermediate step. Finally, we evaluate the performance of the proposed
model on two benchmark datasets - MNIST and XRMB. Through extensive
experiments, we find that the proposed model achieves better performance than
the current state-of-the-art techniques on joint common representation learning
and transfer learning tasks.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017
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
Learning Optimization-inspired Image Propagation with Control Mechanisms and Architecture Augmentations for Low-level Vision
In recent years, building deep learning models from optimization perspectives
has becoming a promising direction for solving low-level vision problems. The
main idea of most existing approaches is to straightforwardly combine numerical
iterations with manually designed network architectures to generate image
propagations for specific kinds of optimization models. However, these
heuristic learning models often lack mechanisms to control the propagation and
rely on architecture engineering heavily. To mitigate the above issues, this
paper proposes a unified optimization-inspired deep image propagation framework
to aggregate Generative, Discriminative and Corrective (GDC for short)
principles for a variety of low-level vision tasks. Specifically, we first
formulate low-level vision tasks using a generic optimization objective and
construct our fundamental propagative modules from three different viewpoints,
i.e., the solution could be obtained/learned 1) in generative manner; 2) based
on discriminative metric, and 3) with domain knowledge correction. By designing
control mechanisms to guide image propagations, we then obtain convergence
guarantees of GDC for both fully- and partially-defined optimization
formulations. Furthermore, we introduce two architecture augmentation
strategies (i.e., normalization and automatic search) to respectively enhance
the propagation stability and task/data-adaption ability. Extensive experiments
on different low-level vision applications demonstrate the effectiveness and
flexibility of GDC.Comment: 15 page
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