A regularized deep learning approach for image de-blurring

Conti, Francesco; Minucci, Gabriele; Derakhshan, Naser

doi:10.1145/3109761.3158383

Cited by 2 publications

(1 citation statement)

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“…Blind deconvolution is an inverse problem that still requires sufficiently strong prior constraints, explicit or implicit, to work (Figure 8). As an example of explicit constraints, one can cite the above assumptions about the homogeneity of noise in images, the assumption that optical aberrations are described by Zernike polynomials [76], or directly through special regularizing terms [77]. A good example of implicit constraints is the pretraining generator networks in a GAN or the training discriminator networks that use certain blurry/sharp images sets.…”

Section: Application Of Deep Learning In a Deconvolution Problemmentioning

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: Application Of Deep Learning In a Deconvolution Problemmentioning

confidence: 99%