Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Choi, Jae Kyu"'
In recent years, patch-based image restoration approaches have demonstrated superior performance compared to conventional variational methods. This paper delves into the mathematical foundations underlying patch-based image restoration methods, with
Externí odkaz:
http://arxiv.org/abs/2309.01328
In this paper, we analyze the error estimate of a wavelet frame based image restoration method from degraded and incomplete measurements. We present the error between the underlying original discrete image and the approximate solution which has the m
Externí odkaz:
http://arxiv.org/abs/2208.10416
The finite-rate-of-innovation (FRI) framework which corresponds a signal/image to a structured low-rank matrix is emerging as an alternative to the traditional sparse regularization. This is because such an off-the-grid approach is able to alleviate
Externí odkaz:
http://arxiv.org/abs/2208.04678
Total variation (TV) minimization is one of the most important techniques in modern signal/image processing, and has wide range of applications. While there are numerous recent works on the restoration guarantee of the TV minimization in the framewor
Externí odkaz:
http://arxiv.org/abs/2207.07473
Publikováno v:
In Applied and Computational Harmonic Analysis January 2025 74
Recently, mapping a signal/image into a low rank Hankel/Toeplitz matrix has become an emerging alternative to the traditional sparse regularization, due to its ability to alleviate the basis mismatch between the true support in the continuous domain
Externí odkaz:
http://arxiv.org/abs/2012.06827
Recently, the finite-rate-of-innovation (FRI) based continuous domain regularization is emerging as an alternative to the conventional on-the-grid sparse regularization for the compressed sensing (CS) due to its ability to alleviate the basis mismatc
Externí odkaz:
http://arxiv.org/abs/1911.03150
Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of dimensionality problem, and the generalization issues. One of the main difficulties is that there exists computational cost
Externí odkaz:
http://arxiv.org/abs/1907.10834
Publikováno v:
IEEE Access, 2019
This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a fidelity-embedded generative adversarial network (GAN) to learn a den
Externí odkaz:
http://arxiv.org/abs/1903.06257
Bayesian inference methods have been widely applied in inverse problems, {largely due to their ability to characterize the uncertainty associated with the estimation results.} {In the Bayesian framework} the prior distribution of the unknown plays an
Externí odkaz:
http://arxiv.org/abs/1901.00262