Keras VDSR for Generated image

Overview

This repository is based on Keras-VDSR written by GeorgeSeif. The original model of VDSR is accessible on The author's project page.

The codes in this repository are optimized to up-scale the images which are generated by VAE. CelebA datasets has been used for this model, and returned fine output.

Files

• vdsr.py : main training file
• predict.py : test VDSR on an arbitrary image

Usage

Before you run the model, make directories below.

• checkpoints : checkpoints are going to be saved in this directory
• data : for image data you are using
• model : model architecture and weights are going to be saved in this directory

vdsr.py

Prepare your own dataset. Set DATA_X_PATH, DATA_Y_PATH, TARGET_IMG_SIZE variables. DATA_X_PATH : Path of input images (jpg) (+) recommend to use blurred image of Y DATA_Y_PATH : Path of output images (jpg) TARGET_IMG_SIZE : Size of output imgaes Default size of input image data is 64x64(RBG), but you can use your own dataset. It will resize the image to TARGET_IMG_SIZE anyway.

You need to add code below to keras/losses.py file to use PSNR as loss function.

def tf_log10(x):
  numerator = tf.log(x)
  denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
  return numerator / denominator

def psnr(y_true, y_pred):
    max_pixel = 1.0
    return 100 - 10.0 * tf_log10((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true))))

predict.py

Recommend to use this file in command prompt. This file has 4 argvs. json_path : model architecture (json) w_path : model weight (hdf5) img_path : arbitrary face images to refine dst_path : path to save the refined images

if you are not using (128, 128) but others for output images, you should edit target_size as well.

Model Structure

There's no ground truth image when you are up-scaling the generated image. So, this model focuses on creating natural detail of input image.

The model has 5 residual blocks. Each block has 4 conv layers. With first and last conv layer, the model has total 22 conv layers.

It uses PSNR(Peak Signal to Noise Ratio) for loss function.