We present a convolutional neural network for image super-resolution. The network directly learns an end-to-end mapping between low- and high-resolution images, ...
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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 ...
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Dec 31, 2014 · 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
Our method directly learns an end-to-end mapping be- tween the low/high-resolution images. The mapping is represented as a deep convolutional neural network ( ...
Missing: farshidfarhat | Show results with:farshidfarhat
Mar 27, 2014 · 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
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
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Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) ...
Missing: farshidfarhat | Show results with:farshidfarhat
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
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What is the difference between CNN and DNN?
Deep Neural Networks (DNNs) have densely connected layers with nodes for global feature learning, often applied to various machine learning tasks. Their design lacks the specialized layers for spatial hierarchies seen in CNNs, making them more general-purpose but potentially less efficient for image-specific tasks.
What is super-resolution convolutional neural network?
Super-resolution is the process of enhancing the resolution of an image, or increasing its detail and clarity, usually by adding pixels. The SRCNN model has been a significant advancement in this field. Here's an overview of how it works: Architecture: The SRCNN is a deep convolutional neural network.
Is CNN deep learning or machine learning?
A convolutional neural network (CNN) is a category of machine learning model, namely a type of deep learning algorithm well suited to analyzing visual data.
What is the full form of srcnn?
The proposed Super-Resolution Convolutional Neural Network (SRCNN)...
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