Dec 31, 2014 ˇ We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high- ...
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This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution ...
Missing: farshidfarhat | Show results with:farshidfarhat
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution ...
Missing: farshidfarhat | Show results with:farshidfarhat
Mar 27, 2014 ˇ Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional ...
Missing: farshidfarhat | Show results with:farshidfarhat
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution ...
Missing: farshidfarhat | Show results with:farshidfarhat
People also ask
What is super-resolution convolutional neural network?
Abstract: A deep learning technique for the super-resolution of a single image. With our approach, spatial dependencies are captured and end-to-end mapping between the low/high-resolution images is learned.
Which method can be used to achieve super-resolution in images?
Generative methods for super resolution image reconstruction include generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows (NFs). GANs use a generator network to produce high-resolution images and a discriminator network to judge their realism.
What is super-resolution deep learning?
Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image.
What is the full form of srcnn?
Block diagram of super-resolution convolutional neural network (SRCNN).
Abstract ˇ We propose a deep learning method for single image super-resolution (SR). ˇ The proposed Super-Resolution Convolutional Neural Network (SRCNN) ...
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Abstract—We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end.
Missing: farshidfarhat | Show results with:farshidfarhat
Commercial Light-Field cameras provide spatial and an- gular information, but its limited resolution becomes an im- portant problem in practical use.
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