vumichien commited on
Commit
afd8425
1 Parent(s): 8052031

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +5 -2
README.md CHANGED
@@ -5,9 +5,12 @@ library_name: keras
5
  ---
6
  ## Model description
7
  This repo contains the model and the notebook [Low-light image enhancement using MIRNet](https://keras.io/examples/vision/mirnet/).
8
- Full credits go to [Soumik Rakshit](https://github.com/soumik12345) and reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) with a slight change on hyperparameter.
9
 
10
- 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
 
 
 
 
11
  ## Dataset
12
  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.
13
  ## Training procedure
 
5
  ---
6
  ## Model description
7
  This repo contains the model and the notebook [Low-light image enhancement using MIRNet](https://keras.io/examples/vision/mirnet/).
 
8
 
9
+ Full credits go to [Soumik Rakshit](https://github.com/soumik12345)
10
+
11
+ Reproduced by [Vu Minh Chien](https://www.linkedin.com/in/vumichien/) with a slight change on hyperparameters.
12
+
13
+ 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
14
  ## Dataset
15
  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.
16
  ## Training procedure