The Art of Regenerating Low Res Images Using Neural Networks

  • Post comments:0 Comments
  • Reading time:5 mins read
You are currently viewing The Art of Regenerating Low Res Images Using Neural Networks

This is an article about how to use image processing and principal component analysis (PCA) to generate a new image from a low resolution image. The idea is to use a neural network to perform the operations of PCA, and then process the result with another neural network in order to produce a high quality image.

The algorithm is simple and the code is short. This is mostly due to the fact that I am not trying to make this into a production quality program, just something fun and interesting.

I have been reading a lot of blog articles and I found this one to be the most interesting. It is an article about a computer program that generates images of lower resolution by using neural networks.

The author used this program to find similarities between different low resolution images and the original high definition ones. This is very interesting because it can be used to find out more information about the original image or picture.

This blog will show you how to use the program and apply it in your life. I am going to start by showing you how to install the program on your computer.

There are loads of articles online on how to do various things with neural networks. Some of them are even useful!

However, a lot of them seem to give the impression that they can do anything. The idea is that you train your neural network, and then it magically turns out to be capable of solving any problem you care to mention.

And if you look more closely, that’s not really true at all.

Suppose you want to generate new images using an existing image as a template. You might try to feed the computer an existing image, and then ask it to generate new images that look similar.

At first glance this seems to work fine. Feed in an image of a car, and out pops 3 or 4 others that look similar.

But if you look closer, there’s something kind of weird about these results. To start with, the images don’t actually look like cars any more; they’re just blurry blobs in roughly the same shape as a car. And even stranger, if you feed in an image of a car from one angle but ask for different angles, the software gives you an entirely different blurred blob! It isn’t even trying to make things consistent from one angle to the next — instead it’s just making new random

This is the first in a series of posts on creating a simple neural network. The next post will include a tutorial on using http://caffe.berkeleyvision.org/ to build & train an image processing neural network with Python. This post will walk through creating your own neural network using Python and NumPy.

This article is written for programmers and assumes familiarity with the fundamentals of computer vision. If you are new to machine learning, check out my review of Andrew Ng’s Machine Learning Course on Coursera.

Here is a link to the notebook used to generate the images in this post: https://github.com/mlinnenberg/NFT-Neural-Networks . I’ve also included the code used to generate the images below; it’s only about 30 lines of code!

In this post, I will try to explain how we can create a simple neural network that is able to predict the quality of an image based on the quality of its constituent parts.

Generally, a Convolutional Neural Network (CNN) is used for such tasks. However, as we will see in this article, using a CNN for such tasks is unnecessary complex and not likely to give better performance.

We will use the Python programming language for our experiment and the libraries keras and PIL for image processing. Keras is a high-level neural-network library written in Python by François Chollet, which makes it particularly easy to use CNNs for image processing tasks without having much knowledge about their internal workings. PIL (Python Imaging Library) is used to manipulate images using Python code. A more detailed explanation of the process can be found in Andrej Karpathy’s blogpost: http://karpathy.github.io/2015/05/11/rnn-effectiveness/ .

In this blogpost I will demonstrate that a simple feed forward neural network (FFNN) with three layers can achieve above average results if trained properly. It turned out that three hidden layers were sufficient for my purposes – if you want to improve the performance

The complete code can be found in my github repository.

Let’s begin with a standard convolutional neural network which takes an image as input. We are using the same architecture that is used in the paper “Image generation with deep recurrent neural networks” by Odena et al, 2016. In this paper, an image of a bird is generated by a recurrent neural network. The model consists of LSTM layers and use a 1D convolution layer. They use a pre-trained VGG network as their starting point, but it’s not necessary to use the pre-trained weights for our purposes here.

To train your own network, you can either use the pretrained weights from the paper or start from scratch and train your own model from scratch. To do so, you will need to specify the number of epochs (epochs = number of times you want to run over all the training data points) and batch size (batches = number of training examples per epoch). I used 20000 for both epochs and batches for this example. Make sure you set “seed” to some random value so you get different results every time you run your script!!!

Leave a Reply