Mar 11, 2024 · A high-generalized super-resolution framework by coupling image and kernel prior learning, i.e. SR-CIKPL, is proposed.
A high-generalized super-resolution framework, i.e. SR-CIKPL, is proposed. •. A local-to-global image prior learning network has been devised.
Mar 22, 2024 · We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning ...
Mar 11, 2024 · Deep convolution neural networks (DCNNs) have demonstrated great success on single image super resolution, where most existing methods aim ...
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Xian-Hua Han , Kazuhiro Yamawaki, Huiyan Jiang : Coupled image and kernel prior learning for high-generalized super-resolution.
Dec 13, 2022 · This study proposes an universal blind SR framework for adaptively and simultaneously predicting the underlying HR image and the counterpart ...
Missing: generalized | Show results with:generalized
We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural ...
This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pre-training-free learning-based ...
Apr 25, 2024 · We propose a blind SR network, capable of combining kernel estimation with structural prior knowledge to reconstruct the textures with high self ...
Missing: Coupled | Show results with:Coupled
Jul 27, 2022 · Abstract—Current deep image super-resolution (SR) ap- proaches attempt to restore high-resolution images from down-.