Deep learning 09-Performance of perceptual losses for super resolution

    Have you ever scratch your head when upscaling low resolution images? I do, because we all know the quality of the images after upscaling degrade. Thanks to the rise of machine learning in recent years, we are able to upscale single image with better results compare with traditional solutions(ex : bilinear, bicubic. You do not need to know what they are except they are apply widely in many products), we call this technique super resolution.

    This sound great, but how could we do it?I did not know it either until I study the tutorials of part2 of the marvelous Practical Deep learning for Coders, this course is fantastic to get your feet wet on deep learning.

    I will try my best to explain everything with minimal prerequisite knowledge on machine learning and computer vision, however, some knowledge of convolution neural network(cnn) is needed. The course of part1 is excellent if you want to learn cnn in depth. If you are in a hurry, pyimagesearch and medium has a short tutorial about cnn.

What is super resolution and how does it work

Q : What is super resolution

A :  Super resolution is a class of technique to enhance the resolution of images or videos.

Q : There are many softwares could help us upscale images, why do we need super resolution?

A : Traditional solutions of upscaling image apply interpolation algorithm on one image only(ex: bilinear or bicubic). In the contrast, super resolution exploit info from another source, either from contiguous frames, from the model trained by machine learning or different scale from one image.

Q : How does super resolution work

A : Super resolution I want to introduce today is based on Perceptual losses for Real-Time style Transfer and Super-Resolution.(please consult wiki if you want to study another type of super resolution).  The most interesting part of this solution is it treat super resolution as an image transformation problem(it is a process where an input image is transformed into an output image). This mean we may use the same technique to solve colorization, denoising, depth estimation, semantic segmentation and another tasks(It is not a problem if you do not know what they are).

Q : How do we transformed low resolution image to high resolution image?

A : A picture worth a thousand words.

    This network is composed by two components, image transformation network and a loss network. Image transformation network transform low resolution image into high resolution image, while loss network measuring the difference between predicted high resolution image and the true high resolution image

Q : What is the loss network anyway?Why do we use it to measure the loss?

A : Loss network is an image classification network train on imagenet (ex : vgg16, resnet, densenet). We use it to measure the loss because we want our network to better measure perceptual and semantic difference between images. The paper call the loss measure by this loss network perceptual loss.

Q : What makes the loss network able to generate better loss?

A : The loss network can generate better loss because the convolutional neural network trained for image classification have already learned to encode the perceptual and semantic information we want.

Q : The color of the image is different after upscale, how could I fixed it?

A : You could apply histogram matching as the paper mentioned, this should be able to deal with most of the cases.

Q : Any draw back of this algorithm?

A : Of course, nothing is perfect.

1 : Not all of the image work, they may look very ugly after upscale.
2 : The image maybe ice cream to your eyes, but it is not reconstructing the photo exactly but create details based on its training from example images.It is impossible to reconstruct the image with perfect results, because we have no way to retrieve the information did not exist from the beginning.
3 : Color of part of the images change after upscale, even histogram matching cannot fix it.

Q : What is histogram matching?

A : It is a way to make the color distribution of image A looks like image B.

Experiment

    All of the experiments use same network architecture and train on 80000 images from imagenet, 2 epoch. From left to right are original image, image upscale 4x by bicubic, image upscale by super resolution by 4x.

    The results are not perfect, but this is not the end, super resolution is a hot research topic, every paper is a stepping stone for next algorithm, we will see more and more better, advance techniques pop out in the future.

Sharing trained model and codes

1 : Notebook to transform the imagenet data to training data
2 : Notebook to train and use the super resolution model
3 : Network model with transformation network and loss network, trained on 80000 images

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