Thanks to Artificial Intelligence and Machine Learning, Deep-Image.ai is trained to provide desired results in various business scenarios. We can teach algorithm-specific use cases based on a client's needs.
Deep-Image.ai uses its GPU hardware infrastructure to deliver results to clients in Europe and America. We have enough capacity to process hundreds of thousands of images monthly.
We prepare a dedicated infrastructure for larger volumes of transformations; the service can also be hosted in cloud environments.
The core of the application is a CNN or ConvNet. In machine learning, it is a class of convolutional neural networks. CNNs are designed to require minimal pre-processing compared to other image classification algorithms.
The convolutional networks are based on biological processes modeled on human neurons. In Deep-image.ai networks are developing. The more examples they get (they analyze more data), the more intelligent the application will be.
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. By default, filtering techniques e.g. bicubic interpolation (using Photoshop or other tools), are used to improve the value.
In Deep-Image.ai, thanks to the use of machine learning, we get a larger image with a much better quality compared to bicubic interpolation. By using the super-resolution (SR) technique the application reconstructs the image, or sequence with a higher resolution, from the low resolution (LR) ones.