图像超分辨实现，从给定的低分辨率(LR)图像中恢复高分辨率(HR)图像的过程

# Superresolution using an efficient sub-pixel convolutional neural network This example illustrates how to use the efficient sub-pixel convolution layer described in ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al.](https://arxiv.org/abs/1609.05158) for increasing spatial resolution within your network for tasks such as superresolution. ``` usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--batchSize BATCHSIZE] [--testBatchSize TESTBATCHSIZE] [--nEpochs NEPOCHS] [--lr LR] [--cuda] [--threads THREADS] [--seed SEED] PyTorch Super Res Example optional arguments: -h, --help show this help message and exit --upscale_factor super resolution upscale factor --batchSize training batch size --testBatchSize testing batch size --nEpochs number of epochs to train for --lr Learning Rate. Default=0.01 --cuda use cuda --threads number of threads for data loader to use Default=4 --seed random seed to use. Default=123 ``` This example trains a super-resolution network on the [BSD300 dataset](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/), using crops from the 200 training images, and evaluating on crops of the 100 test images. A snapshot of the model after every epoch with filename model_epoch_<epoch_number>.pth ## Example Usage: ### Train `python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 30 --lr 0.001` ### Super Resolve `python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_500.pth --output_filename out.png`