Gianmarco Dragonetti
Deep Learning methods for Compressed Sensing image reconstruction.
Rel. Enrico Magli, Diego Valsesia. Politecnico di Torino, Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro), 2020
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Abstract
Deep Learning methods for Compressed Sensing image reconstruction Deep learning (DL) has become, in the last years, the principal way to process images in all the fields of image elaboration and the same goes for Compressive Sensing (CS), the new technique that is making its way in the world of image processing. Compressive Sensing is a full of potential technique, above all for what concerns the acquisition of images, in the case of MRI for example, or the transmission of very big images, e.g. the transmission of the images from satellites. In fact, CS, allowing, together with the single-pixel camera, to directly acquire very compressed data, avoid a waste of time and computational resources to acquire and compress images.
Traditional compression techniques are not universal techniques and they suit up for the data, understanding each time how to compress it in the better way
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