Object Classification

Object classification involves assigning a label to an entire object or photograph.

It is a more advanced version of Image Detection. In Object Classification the neural network has to process different images with different objects, detect them and classify by the type of the item on the picture.

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Image Super-Resolution

Image super-resolution is the traditionally impossible task of generating a new version of an image with higher resolution and detail than the original image.

Frequently models developed for image super-resolution can be used for image restoration and inpainting as they solve related problems.

Datasets often involve using existing photo datasets and creating down-scaled versions of photos for which the models learn to create super-resolution versions.

Object Detection

Object detection is the task of image classification with localization. Any one image may contain multiple objects of different types each of which requires localization and classification. This multiplicity of objects makes this a far more challenging task than simple image classification.

At Enigma Pattern we apply various techniques for image classification with localization to provide the best possible results.

Image Reconstruction

Image reconstruction and image inpainting is the task of filling in missing or corrupt parts of an image.

This task can be thought of as a type of photo filter or transformation that may not have an objective evaluation.

Examples include reconstructing old, damaged black and white photographs and movies.

We often use datasets comprising existing similar photos. During the training phase of the process we create corrupted versions of existing photos that models then learn to repair.