Results 11 to 20 of about 3,652,577 (286)
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization [PDF]
European Conference on Computer Vision, 2023 Diffusion models have demonstrated impressive performance in various image generation, editing, enhancement and translation tasks. In particular, the pre-trained text-to-image stable diffusion models provide a potential solution to the challenging ...
Tao Yang +3 more
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [PDF]
Computer Vision and Pattern Recognition, 2016 Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at ...
C. Ledig +8 more
semanticscholar +1 more source
SinSR: Diffusion-Based Image Super-Resolution in a Single Step [PDF]
Computer Vision and Pattern Recognition, 2023 While super-resolution (SR) methods based on diffusion models exhibit promising results, their practical application is hindered by the substantial number of required inference steps.
Yufei Wang +9 more
semanticscholar +1 more source
Nature Communications, 2021 The trans-Golgi network (TGN) serves as a platform to sort and transport proteins to their final destinations. Here the authors show that the TGN of Arabidopsis consists of spatially and temporally distinct subregions and propose that these zones may ...
Yutaro Shimizu +12 more
doaj +1 more source
eLife, 2023 Although budding yeast has been extensively used as a model organism for studying organelle functions and intracellular vesicle trafficking, whether it possesses an independent endocytic early/sorting compartment that sorts endocytic cargos to the endo ...
Junko Y Toshima +6 more
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]
Computer Vision and Pattern Recognition, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi +7 more
semanticscholar +1 more source
Residual Dense Network for Image Super-Resolution [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well.
Yulun Zhang +4 more
semanticscholar +1 more source
Bio-Protocol, 2020 Many questions in cell biology can be solved by state-of-the-art technology of live cell imaging. One good example is the mechanism of membrane traffic, in which small membrane carriers are rapidly moving around in the cytoplasm to deliver cargo proteins
Kazuo Kurokawa, Akihiko Nakano
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IEEE Access, 2021 The purpose of single-image super resolution (SISR) is to reconstruct an accurate high-resolution image from a degraded low-resolution image. Owing to the lack of information in low-resolution images, SISR is a challenging problem.
Jiun Lee, Inyong Yun, Jaekwang Kim
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Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [PDF]
IEEE International Conference on Computer Vision, 2021 It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
K. Zhang +3 more
semanticscholar +1 more source