2,374 research outputs found
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
Fast and easy blind deblurring using an inverse filter and PROBE
PROBE (Progressive Removal of Blur Residual) is a recursive framework for
blind deblurring. Using the elementary modified inverse filter at its core,
PROBE's experimental performance meets or exceeds the state of the art, both
visually and quantitatively. Remarkably, PROBE lends itself to analysis that
reveals its convergence properties. PROBE is motivated by recent ideas on
progressive blind deblurring, but breaks away from previous research by its
simplicity, speed, performance and potential for analysis. PROBE is neither a
functional minimization approach, nor an open-loop sequential method (blur
kernel estimation followed by non-blind deblurring). PROBE is a feedback
scheme, deriving its unique strength from the closed-loop architecture rather
than from the accuracy of its algorithmic components
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
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images
In recent years, deep neural network-based restoration methods have achieved
state-of-the-art results in various image deblurring tasks. However, one major
drawback of deep learning-based deblurring networks is that large amounts of
blurry-clean image pairs are required for training to achieve good performance.
Moreover, deep networks often fail to perform well when the blurry images and
the blur kernels during testing are very different from the ones used during
training. This happens mainly because of the overfitting of the network
parameters on the training data. In this work, we present a method that
addresses these issues. We view the non-blind image deblurring problem as a
denoising problem. To do so, we perform Wiener filtering on a pair of blurry
images with the corresponding blur kernels. This results in a pair of images
with colored noise. Hence, the deblurring problem is translated into a
denoising problem. We then solve the denoising problem without using explicit
clean target images. Extensive experiments are conducted to show that our
method achieves results that are on par to the state-of-the-art non-blind
deblurring works.Comment: Accepted at ICIP 202
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