Extensions of the Justen–Ramlau blind deconvolution method

Hearn, Tristan A.; Reichel, Lothar

doi:10.1007/s10444-012-9290-z

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…In ref. [40], Tikhonov's regularization was used with the Sobolev smoothing operator under periodic boundary conditions (this method was extended in [41]). Since the modern problems facing optics, computer vision, and astronomy are often blind deconvolution problems, the review will focus on the corresponding methods.…”

Section: Deconvolution Methods Classificationmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

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In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

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Section: Deconvolution Methods Classificationmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

View full text Add to dashboard Cite

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“…Periodic boundary conditions were assumed in [62]. The work in [62] was extended in [49] to cover the Neumann and anti-reflective boundary conditions.…”

Section: Other Methodsmentioning

confidence: 99%