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Super resolution is the task of taking an input of a low resolution (LR) and upscaling it to that of a high resolution. You can find relevant leaderboards in the subtasks below. Credit MemNet) Benchmarks Add a Result These leaderboards are used to track progress in Super-Resolution Libraries.
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An official PyTorch implementation of "Explorable Super Resolution " by Yuval Bahat and Tomer Michaeli (CVPR 2020). Table of Contents. Overview; Dependencies; Acknowledgement; . CEM) can be used as a standalone component, to wrap any super-resolution model, whether before or after its training, for guranteeing the consistency of its outputs. Real-ESRGAN - Real-ESRGAN.
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A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc. pytorch-examplesREADME.md at main &183; ammuajpytorch-examples.
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Im not a native speaker. There may be some grammar or vocabulary misuse. Sorry about that. Im doing a masters degree and researching on MR human brain image super-resolution with DL model. After surveying SR papers, most of them obtain LR images by down-sampling(bicubic or k-space degrade). So I design a supervised 2D CNN-based model.
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ESPCN (Efficient Sub-Pixel CNN), proposed by Shi, 2016 is a model that reconstructs a high-resolution version of an image given a low-resolution version. It leverages efficient "sub-pixel convolution" layers, which learns an array of image upscaling filters. In this code example, we will implement the model from the paper and train it on a.
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Convolutional neural networks have been proven to be of great benefit for single- image super-resolution (SISR). However, previous works do not make full use of multi-scale features and ignore the inter-scale correlation between different upsampling factors, resulting in sub-optimal performance. Instead of blindly increasing the depth of the network, we are.
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