SRZoo is a collection of toolkits and models for deep learning-based image super-resolution. It provides various pre-trained state-of-the-art super-resolution models that are ready for use.

Here are the key features of SRZoo:

• SRZoo provides official pre-trained models of various super-resolution methods.
• With SRZoo, you can easily obtain the super-resolved images from the supported super-resolution methods.
• It is possible to employ the super-resolution models in various environments such as GPUs supporting CUDA and web browsers via TensorFlow.js.
• It is possible to compare the performance of the super-resolution methods with the same evaluation metrics and the same environment.

You can find our motivation and some detailed description of SRZoo such as performance comparison in the following paper.

• J.-H. Choi, J.-H. Kim, J.-S. Lee. SRZoo: an integrated repository for super-resolution using deep learning. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020 [Paper] [arXiv]

• Python 3.6 or newer
• TensorFlow 1.12 or newer

We currently provide the following pre-trained super-resolution models, where the model parameters are provided by the original authors. Please cite the paper of the original authors when you use the models.

SRZoo offers a simple image retrieval via get_sr.py, e.g.,

python get_sr.py --config_path=configs/edsr.json --model_path=edsr_x4.pb --input_path=LR --output_path=SR --scale=4

Arguments:

• config_path: Path of the model config file.
• model_path: Path of the pre-trained model file.
• input_path: Path of the input low-resolution images.
• output_path: Path of the output super-resolved images will be saved.
• scale: Upscaling factor.
• self_ensemble: Specify this to employ geometric self-ensemble.
• cuda_device: CUDA device index to be used (will be set to the environment variable 'CUDA_VISIBLE_DEVICES').

※ Some models can be run only on GPUs due to the different ordering of the dimensions.

With the obtained super-resolved images, it is possible to evaluate the performance via evaluate_sr.py, e.g.,

python evaluate_sr.py --sr_path=SR --truth_path=HR

Arguments:

• sr_path: Path of the super-resolved images.
• truth_path: Path of the ground-truth images.
• shave_borders: The amount of shaving pixles on borders of the images. It is usually set to the upscaling factor.
• color_mode: Color conversion mode. ycbcry: Y channel of the YCbCr color space. rgb: RGB channels of the RGB color space.
• evaluators: Comma-separated evaluation methods. The evaluators in the evaluators/ folder will be used.
• ouptut_name: Filename of the output CSV file.

You can also write your own evaluation metric by implementing an evaluator class that inherits BaseEvaluator in the evaluators/ folder.

It is possible to convert the other pre-trained super-resolution models. Please refer to the converter/ folder for more information. In addition, please refer to the config/ folder to write your own model config file along with the converted model.

We also provide the downscaling utilities for evaluating the super-resolution models, which are in the utils/downscale/ folder.

Since SRZoo is developed to deal with models considering images as both inputs and outputs, our repository can be used to employ the other image processing algorithms with only a few modifications. As a proof-of-concept, we provide a pre-trained deep learning-based image compression model in SRZoo.

To use these models, simply set the upscaling factor (e.g., --scale option of get_sr.py) to 1. Note that only the GPU mode is currently supported for these models.