| library_name: tf-keras | |
| tags: | |
| - image-to-image | |
| ## Model description | |
| This repo contains the model and the notebook [Low-light image enhancement using MIRNet](https://keras.io/examples/vision/mirnet/). | |
| Full credits go to [Soumik Rakshit](https://github.com/soumik12345) | |
| Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) with a slight change on hyperparameters. | |
| With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as photography, security, medical imaging, and remote sensing. The MIRNet model for low-light image enhancement is a fully-convolutional architecture that learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details | |
| ## Dataset | |
| The [LoL Dataset](https://drive.google.com/uc?id=1DdGIJ4PZPlF2ikl8mNM9V-PdVxVLbQi6) has been created for low-light image enhancement. It provides 485 images for training and 15 for testing. Each image pair in the dataset consists of a low-light input image and its corresponding well-exposed reference image. | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-04 | |
| - train_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: ReduceLROnPlateau | |
| - num_epochs: 50 | |
| ### Training results | |
| - The results are shown in TensorBoard (Training metrics). | |
| ### View Model Demo | |
|  | |
| <details> | |
| <summary> View Model Plot </summary> | |
|  | |
| </details> |