293 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
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
Motion Deblurring in the Wild
The task of image deblurring is a very ill-posed problem as both the image
and the blur are unknown. Moreover, when pictures are taken in the wild, this
task becomes even more challenging due to the blur varying spatially and the
occlusions between the object. Due to the complexity of the general image model
we propose a novel convolutional network architecture which directly generates
the sharp image.This network is built in three stages, and exploits the
benefits of pyramid schemes often used in blind deconvolution. One of the main
difficulties in training such a network is to design a suitable dataset. While
useful data can be obtained by synthetically blurring a collection of images,
more realistic data must be collected in the wild. To obtain such data we use a
high frame rate video camera and keep one frame as the sharp image and frame
average as the corresponding blurred image. We show that this realistic dataset
is key in achieving state-of-the-art performance and dealing with occlusions
Deep Mean-Shift Priors for Image Restoration
In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.Comment: NIPS 201
Learning Deep CNN Denoiser Prior for Image Restoration
Model-based optimization methods and discriminative learning methods have
been the two dominant strategies for solving various inverse problems in
low-level vision. Typically, those two kinds of methods have their respective
merits and drawbacks, e.g., model-based optimization methods are flexible for
handling different inverse problems but are usually time-consuming with
sophisticated priors for the purpose of good performance; in the meanwhile,
discriminative learning methods have fast testing speed but their application
range is greatly restricted by the specialized task. Recent works have revealed
that, with the aid of variable splitting techniques, denoiser prior can be
plugged in as a modular part of model-based optimization methods to solve other
inverse problems (e.g., deblurring). Such an integration induces considerable
advantage when the denoiser is obtained via discriminative learning. However,
the study of integration with fast discriminative denoiser prior is still
lacking. To this end, this paper aims to train a set of fast and effective CNN
(convolutional neural network) denoisers and integrate them into model-based
optimization method to solve other inverse problems. Experimental results
demonstrate that the learned set of denoisers not only achieve promising
Gaussian denoising results but also can be used as prior to deliver good
performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing
Remote sensing images are essential for many earth science applications, but
their quality can be degraded due to limitations in sensor technology and
complex imaging environments. To address this, various remote sensing image
deblurring methods have been developed to restore sharp, high-quality images
from degraded observational data. However, most traditional model-based
deblurring methods usually require predefined hand-craft prior assumptions,
which are difficult to handle in complex applications, and most deep
learning-based deblurring methods are designed as a black box, lacking
transparency and interpretability. In this work, we propose a novel blind
deblurring learning framework based on alternating iterations of shrinkage
thresholds, alternately updating blurring kernels and images, with the
theoretical foundation of network design. Additionally, we propose a learnable
blur kernel proximal mapping module to improve the blur kernel evaluation in
the kernel domain. Then, we proposed a deep proximal mapping module in the
image domain, which combines a generalized shrinkage threshold operator and a
multi-scale prior feature extraction block. This module also introduces an
attention mechanism to adaptively adjust the prior importance, thus avoiding
the drawbacks of hand-crafted image prior terms. Thus, a novel multi-scale
generalized shrinkage threshold network (MGSTNet) is designed to specifically
focus on learning deep geometric prior features to enhance image restoration.
Experiments demonstrate the superiority of our MGSTNet framework on remote
sensing image datasets compared to existing deblurring methods.Comment: 12 pages
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
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