Skip to content

Latest commit

 

History

History
51 lines (33 loc) · 1.71 KB

README.md

File metadata and controls

51 lines (33 loc) · 1.71 KB

SuperRes-GAN

Fifth Semester mini project.Video Super Resolution using Deep Convolutional Generative Adversarial Network(DCGAN).

A Generative Adversarial Network has two main Neural Networks.

  • Generator
  • Discriminator

Generator

The Generator takes a low resolution(LR) image as input and tries to generate a high resolution(HR) image for that input.

Discriminator

The Discriminator learns from the actual training data and also the gererated results of the Generator and tries to distinguish real and generated images.

Instructions for Usage

Prepare train and test npy files

python3 prepare.py

Train the model

python3 train.py

Working of the Model

As the training goes the generator gets better and better at generating realistic outputs while the discriminator finds it harder and harder to distingush real and fake images.

Here are some of the results of super resolution on images from our testing dataset: Left image is super resolution using bilinear interpolation and Right image is the output of our GAN

comic
butterfly
lenna

Datasets

Training Dataset

We used The Validation Set from ILSVRC2012(Image Recognition Challenge).This Set Contains 50000 randomly selected Images from Imagenet dataset.We are only using 10000 of these images for our model.You can find the dataset here.

Testing Dataset

Our Testing dataset contains images from various datasets like Set5, Set14, BSDS100, Urban100, Manga109, Historical.The files can be found here