Abstract: A generative system for the creation of digital images for printing on design surfaces comprises a training dataset comprising a plurality of sample images for printing on design surfaces, a generative adversarial network comprising a generator and a discriminator, wherein the generator receives noise at input and is trained to generate at output starting from the noise a new artificially generated image adapted to be used for printing on design surfaces, and wherein the discriminator receives at input the new artificially generated image and is trained to compare and distinguish the new image generated by the sample images of the training dataset.

Abstract: A computer-implemented system for image quality enhancement and color spaces conversion for manufactured surfaces printers comprises at least a convolutional neural network, configured for executing at least the following steps: receive in input an original image x? in RGB space; from the original image x?, elaborate an approximation {tilde over (x)}+? in multichannel space or in RGB space of an enhanced image x+? for manufactured surfaces printers; and return to output the approximation {tilde over (x)}+? of the enhanced image x+? for the use in manufactured surfaces printers.

Abstract: A generative system for the creation of digital images for printing on design surfaces comprises a training dataset comprising a plurality of sample images for printing on design surfaces, a generative adversarial network comprising a generator and a discriminator, wherein the generator receives noise at input and is trained to generate at output starting from the noise a new artificially generated image adapted to be used for printing on design surfaces, and wherein the discriminator receives at input the new artificially generated image and is trained to compare and distinguish the new image generated by the sample images of the training dataset.