人工神经网络图像变换

Question

I have a pairs of images (input-output) but I don't know the transformation to going from A (input) to B (output). I want to record image A and get image B. Physically I can change the setup to get A or B, but I want to do it by software.

If I understood well, a trained Artificial Neural Network is able to do that, having an input can give the corresponding output, is it right? Is there any software/ANN that just "training" it with entering a number of input-output pairs will be able to provide the correct output if the input is a new (but similar to the others) image?

Thanks

Answer 1

If you have some relevant amount of image pairs (input/output pair) and you don't know transformation between input and output you could train ANN on that training set to imitate that unknown transformation. You will be able to well train your ANN only if you have sufficient amount of training image pairs, but it could be pretty impossible when that unknown transformation is complicated.

For example if that transformation simply increases intensity values of pixels at input image by given value, ANN will very fast learn to imitate that behavior, but if that unknown transformation is some complicated convolution or few serial convolutions or something more complicated it will be very hard, near impossible to train ANN to imitate that transformation. So, more complex transformation will need bigger training set and more complex ANN design.

There are plenty of free opensource ANN libraries implemented in many languages. You could start for example with that tutorial: http://www.codeproject.com/Articles/13091/Artificial-Neural-Networks-made-easy-with-the-FANN

Answer 2

What you are asking is possible in principle -- in theory, an ANN with sufficiently many hidden units can learn an arbitrary function to map inputs to outputs. However, as the comments and other answers have mentioned, there may be many technical issues with your particular problem that could make it impractical. I would classify these problems as (a) mapping complexity, (b) model complexity, (c) scaling complexity, and (d) implementation complexity. They are all somewhat related, but hopefully this is a useful way to break things down.

Mapping complexity

As mentioned by Springfield762, there are many possible functions that map from one image to another image. If the relationship between your input images and your output images is relatively simple -- like increasing the intensity of each pixel by a constant amount -- then an ANN would be able to learn this mapping without much difficulty. There are probably many more transformations that would be similarly easy to learn, such as skewing, flipping, rotating, or translating an image -- basically any affine transformation would be easy to learn. Other, nonlinear transformations could also be feasible, such as squaring the intensity of each pixel.

As a general rule, the more complicated the relationship between your input and output images, the more difficult it will be to get a model to learn this mapping for you.

Model complexity

The more complex the mapping from inputs to outputs, the more complex your ANN model will be to be able to capture this mapping. Models with many hidden layers have been shown in the past 10 years to perform quite well on tasks that people had previously thought impossible, but often these state-of-the-art models have millions or even billions of parameters and take weeks to train on GPU hardware. A simple model can capture many simple mappings, but if you have a complex input-output map to learn, you'll need a large, complex model.

Scaling complexity

Yves mentioned in the comments that it can be difficult to scale models up to typical image sizes. If your images are relatively small (currently the state of the art is to model images on the order of 100x100 pixels), then you can probably just throw a bunch of raw pixel data at an ANN model and see what happens. But if you're using 6000x4000 images from your shiny Nikon DSLR, it's going to be quite difficult to process those in a reasonable amount of time. You'd be better off compressing your image data somehow ( PCA is a common technique) and then trying to learn the mapping in the compressed space.

In addition, larger images will have a larger space of possible mappings between them, so you'll need more of your larger images as training data than you would if you had small images.

Springfield762 also mentioned this: If the mapping between your input and output images is simple, then you'll only need a few examples to learn the mapping successfully. But if you have a complicated mapping, then you'll need much more training data to have a chance at learning the mapping properly.

Implementation complexity

It's unlikely that a tool already exists that would let you just throw image data into an ANN model and have a mapping appear. Most likely you'll need, at a minimum, to implement some code that will pre-process your image data. In addition, if you have lots of large images you'll probably need to write code to handle loading data from disk, etc. (There are a lot of "big data" tools for things like this, but they all require some amount of work to get set up.)

There are many, many open source ANN toolkits out there nowadays. FANN (already mentioned) is a popular one in C++ with bindings in other languages. Caffe is quite popular, and is also implemented in C++ with bindings. There seem to be many toolkits that use Python and Theano or some other GPU acceleration library -- Keras , Lasagne , Hebel , Pylearn2 , neon , and Theanets (I wrote this one). Many people use Torch , written in Lua. Matlab has at least one neural network toolbox. I'm less familiar with other ecosystems, but Java seems to have Deeplearning4j , C# has Accord , and even R has darch .

But with any of these neural network toolkits, you're going to have to write some code to load the data, process it into the appropriate input format, construct (or load) a network model, train the model, etc.

Answer 3

The problem you're trying to solve is a canonical classification problem that neural networks can help you solve. You treat the B images as a set of labels that you match to A, and once trained, the neural network will be able to match the B images to new input based on where the network locates new input in a high-dimensional vector space. I assume you'd use some combination of convolutional networks to create your features, and softmax for multinomial classification on the output layer. More here: http://deeplearning4j.org/convolutionalnets.html

Answer 4

由于这已经写了，在线虫领域（条件生成对抗网络）已经做了很多工作，请参考： https ：//arxiv.org/pdf/1611.07004.pdf