2022
DOI: 10.1051/ro/2022137
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An improved PDE-constrained optimization fluid registration for image multi-frame super resolution

Abstract: The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on a partial differential equation (PDE)-constrained optimization fluid image registration and we use a fourth order PDE to treat both the registration and restoration steps that guarantee the success of SR alg… Show more

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Cited by 5 publications
(3 citation statements)
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“…Proof. By taking a minimizing sequence (X j ) j∈N for (23) and considering X j (x) to be the image at pixel position x, then there exist three non-negative constants denoted by M 1 , M 2 and M 3 verifying:…”
Section: Numerical Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…Proof. By taking a minimizing sequence (X j ) j∈N for (23) and considering X j (x) to be the image at pixel position x, then there exist three non-negative constants denoted by M 1 , M 2 and M 3 verifying:…”
Section: Numerical Algorithmmentioning
confidence: 99%
“…For instance, Tikhonov regularization [34] is commonly used and can be implemented as a one-step filter using Fourier transform in the case of image deconvolution. However, this approach often amplifies high frequencies where noise is prominent, leading to suboptimal results, and Total Variation (TV) is widely applied in various image processing applications, such as blind deconvolution [3,25], inpainting [11], and super-resolution [29,23], due to its ability to preserve edges. However, total variation based methods often fail to retain fine structures, intricate details, and textures present in the original image [26,27].…”
mentioning
confidence: 99%
“…It restricts the inter-frame motion to be translational because the discrete Fourier transform assumes uniformly spaced samples, another drawback is that the considered prior knowledge is frequently difficult to express in the frequency domain. • The spatial domain on the other hand, which is discussed in [29,20,28] makes it possible to integrate priors on the image.…”
mentioning
confidence: 99%