Reference-based dual-task framework for motion deblurring

Liu, Cunzhe; Hua, Zhen; Li, Jinjiang

doi:10.1007/s00371-023-02771-8

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…This can be achieved using the same above-mentioned methodology using the conditional score s t (x t , y) = ∇ log p t (•|y)(x t ). The conditional score can be approximated score-matching in two ways: 1) by conditional score-matching of a NN S θ (x t , y, t) that takes as input both the image x t for all t and the measurement y [128], [132], [133] (supervised) and 2) unconditional score-matching of S θ (x t , t) (c.f. previous paragraph) combined with the Bayes rule and an approximation of the posterior distribution p(y|x t ) [134], [135] (unsupervised).…”

Section: E Diffusion Modelsmentioning

confidence: 99%

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches

Bousse,

Kandarpa,

Shi

et al. 2024

IEEE Trans. Radiat. Plasma Med. Sci.

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Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.

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Section: E Diffusion Modelsmentioning

confidence: 99%