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Model description

This repo contains the model and the notebook Low-light image enhancement using MIRNet.

Full credits go to Soumik Rakshit

Reproduced by Vu Minh Chien 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 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).

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