933 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
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
Data-Driven Image Restoration
Every day many images are taken by digital cameras, and people
are demanding visually accurate and pleasing result. Noise and
blur degrade images captured by modern cameras, and high-level
vision tasks (such as segmentation, recognition, and tracking)
require high-quality images. Therefore, image restoration
specifically, image
deblurring and image denoising is a critical preprocessing step.
A fundamental problem in image deblurring is to recover reliably
distinct spatial frequencies that have been suppressed by the
blur kernel. Existing image deblurring techniques often rely on
generic image priors that only help recover part of the frequency
spectrum, such as the frequencies near the high-end. To this end,
we pose the following specific questions: (i) Does class-specific
information offer an advantage over existing generic priors for
image quality restoration? (ii) If a class-specific prior exists,
how should it be encoded into a deblurring framework to recover
attenuated image frequencies? Throughout this work, we devise a
class-specific prior based on the band-pass filter responses and
incorporate it into a deblurring strategy. Specifically, we show
that the subspace of band-pass filtered images and their
intensity distributions serve as useful priors for recovering
image frequencies.
Next, we present a novel image denoising algorithm that uses
external, category specific image database. In contrast to
existing noisy image restoration algorithms, our method selects
clean image “support patches” similar to the noisy patch from
an external database. We employ a content adaptive distribution
model for each patch where we derive the parameters of the
distribution from the support patches. Our objective function
composed of a Gaussian fidelity term that imposes category
specific information, and a low-rank term that encourages the
similarity between the noisy and the support patches in a robust
manner.
Finally, we propose to learn a fully-convolutional network model
that consists of a Chain of Identity Mapping Modules (CIMM) for
image denoising. The CIMM structure possesses two distinctive
features that are important for the noise removal task. Firstly,
each residual unit employs identity mappings as the skip
connections and receives pre-activated input to preserve the
gradient magnitude propagated in both the forward and backward
directions. Secondly, by utilizing dilated kernels for the
convolution layers in the residual branch, each neuron in the
last convolution layer of each module can observe the full
receptive field of the first layer
Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution
Incomplete color sampling, noise degradation, and limited resolution are the
three key problems that are unavoidable in modern camera systems. Demosaicing
(DM), denoising (DN), and super-resolution (SR) are core components in a
digital image processing pipeline to overcome the three problems above,
respectively. Although each of these problems has been studied actively, the
mixture problem of DM, DN, and SR, which is a higher practical value, lacks
enough attention. Such a mixture problem is usually solved by a sequential
solution (applying each method independently in a fixed order: DM DN
SR), or is simply tackled by an end-to-end network without enough
analysis into interactions among tasks, resulting in an undesired performance
drop in the final image quality. In this paper, we rethink the mixture problem
from a holistic perspective and propose a new image processing pipeline: DN
SR DM. Extensive experiments show that simply modifying the usual
sequential solution by leveraging our proposed pipeline could enhance the image
quality by a large margin. We further adopt the proposed pipeline into an
end-to-end network, and present Trinity Enhancement Network (TENet).
Quantitative and qualitative experiments demonstrate the superiority of our
TENet to the state-of-the-art. Besides, we notice the literature lacks a full
color sampled dataset. To this end, we contribute a new high-quality full color
sampled real-world dataset, namely PixelShift200. Our experiments show the
benefit of the proposed PixelShift200 dataset for raw image processing.Comment: Code is available at: https://github.com/guochengqian/TENe
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