Inertial self‐adaptive algorithm for solving split feasible problems with applications to image restoration
Sign Up to like & getrecommendations! Published in 2019 at "Mathematical Methods in the Applied Sciences"
DOI: 10.1002/mma.5836
Abstract: We introduce a new self‐adaptive algorithm for applications to image restoration problems. In order to study an image restoration, we consider the algorithm that contains inertial effects and step sizes, which is independent from the… read more here.
Keywords: self adaptive; algorithm; image restoration; adaptive algorithm ... See more keywords
Morphological Component Image Restoration by Employing Bregmanized Sparse Regularization and Anisotropic Total Variation
Sign Up to like & getrecommendations! Published in 2020 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-019-01268-x
Abstract: Image deblurring is a fundamental problem in imaging field which often needs to recover the important structure of images. This paper addresses the image deblurring problem by considering an image as a combination of its… read more here.
Keywords: image; part; image restoration; total variation ... See more keywords
An Explicit Solution for Image Restoration using Markov Random Fields
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Signal Processing Systems"
DOI: 10.1007/s11265-019-01470-9
Abstract: Image restoration and denoising is an essential preprocessing step for almost every subsequent task in computer vision. Markov Random Fields offer a well-founded, sophisticated approach for this purpose, but unfortunately the associated computation procedures are… read more here.
Keywords: random fields; image restoration; markov random;
A general truncated regularization framework for contrast-preserving variational signal and image restoration: Motivation and implementation
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DOI: 10.1007/s11425-017-9260-8
Abstract: Variational methods have become an important kind of methods in signal and image restoration-a typical inverse problem. One important minimization model consists of the squared ℓ2 data fidelity (corresponding to Gaussian noise) and a regularization… read more here.
Keywords: general truncated; framework; image restoration; signal image ... See more keywords
An adaptive method for image restoration based on high-order total variation and inverse gradient
Sign Up to like & getrecommendations! Published in 2020 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-020-01657-9
Abstract: The total variation (TV) regularization model for image restoration is widely utilized due to its edge preservation properties. Despite its advantages, the TV regularization can obtain spurious oscillations in flat regions of digital images and… read more here.
Keywords: image; order; image restoration; method ... See more keywords
Content-aware image restoration for electron microscopy.
Sign Up to like & getrecommendations! Published in 2019 at "Methods in cell biology"
DOI: 10.1016/bs.mcb.2019.05.001
Abstract: Multiple approaches to use deep neural networks for image restoration have recently been proposed. Training such networks requires well registered pairs of high and low-quality images. While this is easily achievable for many imaging modalities,… read more here.
Keywords: aware image; image restoration; content aware; microscopy ... See more keywords
SSIM-based sparse image restoration
Sign Up to like & getrecommendations! Published in 2021 at "Journal of King Saud University - Computer and Information Sciences"
DOI: 10.1016/j.jksuci.2021.07.024
Abstract: Abstract In this paper, we provide a sparse image restoration algorithm with a SSIM-based objective function. The proposed technique is a modification to the SSIM-inspired OMP (iOMP) and, and it has two parallel sparse restoration… read more here.
Keywords: sparse image; image restoration; restoration; based sparse ... See more keywords
Non-convex weighted ℓp nuclear norm based ADMM framework for image restoration
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DOI: 10.1016/j.neucom.2018.05.073
Abstract: Abstract Inspired by the fact that the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies.… read more here.
Keywords: image restoration; nuclear norm; image; non convex ... See more keywords
Effective image restoration for semantic segmentation
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DOI: 10.1016/j.neucom.2019.09.063
Abstract: Abstract Recent semantic segmentation algorithms are greatly accelerated by deep convolutional neural networks (DCNNs). Although most of them perform well on normal images, they are not robust to the degenerations of images. To boost the… read more here.
Keywords: image; image restoration; segmentation; semantic segmentation ... See more keywords
Single image restoration through ℓ2-relaxed truncated ℓ0 analysis-based sparse optimization in tight frames
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DOI: 10.1016/j.neucom.2021.02.053
Abstract: Abstract Image restoration problems, i.e., recovery of an original high-quality image from the degraded observation, arise in various science and engineer areas. Over the past decades, the framelet-based methods are particularly investigated and adopted, owing… read more here.
Keywords: relaxed truncated; image restoration; image; truncated analysis ... See more keywords
Fourier ring correlation simplifies image restoration in fluorescence microscopy
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DOI: 10.1038/s41467-019-11024-z
Abstract: Fourier ring correlation (FRC) has recently gained popularity among fluorescence microscopists as a straightforward and objective method to measure the effective image resolution. While the knowledge of the numeric resolution value is helpful in e.g.,… read more here.
Keywords: image; microscopy; image restoration; ring correlation ... See more keywords