Neighborhood Regression for Edge-Preserving Image Super-Resolution (ICASSP 2015)
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Updated
Sep 20, 2015 - MATLAB
Neighborhood Regression for Edge-Preserving Image Super-Resolution (ICASSP 2015)
Deep Learning based Image Super Resolution using DCGANs in Keras
Torch implementation of the VDSR-CNN Upscaling algorithm
Super Resolution of picture images using deep learning
Matlab simulation of Fourier ptychographic microscopy (FPM).
An implementation of SRGAN model in Keras
A framework for multiframe super-resolution (enhancing the quality of an image from multiple similar low-resolution images) with support for hyperspectral imaging data.
Edge-enhancment Neural Network Implemented in Caffe
Tensorflow implementation of pixel-recursive-super-resolution(Google Brain paper: https://arxiv.org/abs/1702.00783)
Super resolution using CNN based on paper titled "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" (https://arxiv.org/abs/1603.08155)
Image super-resolution through deep learning
Zhimin Tang, Linkai Luo, Hong Peng, Shaohui Li. A joint residual network with paired ReLUs activation for image super-resolution, Neurocomputing (2017). https://doi.org/10.1016/j.neucom.2017.07.061
Enhancing resolution of images without loosing details
Real-time video stream viewer. Works in conjunction with quick-vision-android.
Functional interpolation to create a more meaningful interpolation than bilinear or bicubic methods.
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