Deep Image by TEONITE — the app that uses machine learning to enlarge images, without losing quality
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Have you ever wondered where the innovative ideas come from? In our software house — TEONITE — we usually implement ideas to solve or improve a specific issue. Deep Image, an application created by TEONITE’s full-stack developer — Andrzej, was created by accident to solve a certain problem concerning the quality of graphics files.
Want to know how Deep Image works? Or how it was made? Check our case study HERE
How can Deep Image help you?
Before we describe what Deep Image is check if any of the following applies to you:
- you have legal, archived graphics files of your favorite artists, you would like to have them in the form of a poster, but their resolution does not allow for printing
- you have an archive of private photos in low resolution and you would like to improve their quality
- you need to enlarge or cut out a specific fragment of the photo/graphic (for example, school photography from years ago), but you are worried about the quality
- you work in CSI Miami and you have to enhance the picture from the city camera because in the passersby glasses you can see the criminal’s face reflection
Ok, so how does Deep Image actually works?
Each graphic file is a matrix and a set of data stored in it (numbers — pixels). After enlarging the image, the amount of data does not increase, so the image obtained has visually worse quality. Meanwhile, in Deep Image, thanks to the use of machine learning, we get a larger image with much better quality.
Neurons, modeled on human, in action
The core of the application is a CNN or ConvNet — a class of convolutional neural networks, which are successfully used for image analysis.
The convolutional networks are based on biological processes, modeled on human neurons. In Deep Image networks are developing. The network learns that the line on the graphic can not have sharp edges and should be smooth — more options for smoothing, the better the quality of output file is.
“We need to go deeper!”
Working with neural networks does not look like a standard programming process. More like science, based on showing the pattern.
Want to know how Deep Image works? Or how it was made? Check our case study HERE
Application operation is based on the iterative process, which brings the final graphics almost to perfection. Everything happens automatically through a framework that is used in Deep Image-Keras (high-level API written in Python for the tangled neural networks TensorFlow and Theano).
Remove artifacts / resize
The entire process uses two algorithms developed on the basis of scientific research:
- Zoom algorithm (resize)
- Jpg algorithm (remove artifacts)
Want to know how Deep Image works? Or how it was made? Check our case study HERE
