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Deep learning CT image restoration using system blur and noise models. [PDF]

J Med Imaging (Bellingham)
Yuan Y, Gang GJ, Stayman JW.
europepmc   +1 more source

Research Progress on Color Image Quality Assessment. [PDF]

J Imaging
Gao M, Song C, Zhang Q, Zhang X, Li Y, Yuan F.   +5 more
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A hybrid zero-reference and dehazing network for joint low-light underground image enhancement. [PDF]

Sci Rep
Du Q, Zhang S, Wang Z, Liang J, Yang S.
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Computational single fundus image restoration techniques: a review. [PDF]

Front Ophthalmol (Lausanne)
Zhang S, Webers CAB, Berendschot TTJM.
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Fringe Texture Driven Droplet Measurement End-to-End Network Based on Physics Aberrations Restoration of Coherence Scanning Interferometry. [PDF]

Micromachines (Basel)
Zhang Z, Chen J, Yang H, Yin Z.
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Real-world face super-resolution based on generative adversarial and face alignment networks. [PDF]

Sci Rep
Fathy H, Faheem MT, Elbasiony R.
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ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages. [PDF]

EJNMMI Res
Vashistha R, Moradi H, Hammond A, O'Brien K, Rominger A, Sari H, Shi K, Vegh V, Reutens D.   +8 more
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Surface-Aware Blind Image Deblurring

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Blind image deblurring is a conundrum because there are infinitely many pairs of latent image and blur kernel. To get a stable and reasonable deblurred image, proper prior knowledge of the latent image and the blur kernel is urgently required. Different from the recent works on the statistical observations of the difference between the blurred image ...
Jun Liu, Ming Yan, Tieyong Zeng
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