Abstract: An online content system, such as a digital magazine, includes an image scaling engine for increasing the resolution of images. The image scaling engine comprises a convolutional neural network. An input image is preprocessed for use as inputs to a convolutional neural network (CNN). The preprocessed input image pixel values are used as inputs to the CNN. The CNN comprises convolutional layers and dense layers for determining image features and increasing image resolution. The CNN is trained using backpropagation to adjust model weights and biases. Each convolutional layer of a CNN detects features in an image by comparing image subregions to a set of known kernels and determining similarities between subregions and kernels using a convolution operation. The dense layers of the CNN have full connections to all of the outputs of a previous layer to determine the specific target output result such as output image pixel values.

Abstract: An online content system, such as a digital magazine, includes an image scaling engine for increasing the resolution of images. The image scaling engine comprises a convolutional neural network. An input image is preprocessed for use as inputs to a convolutional neural network (CNN). The preprocessed input image pixel values are used as inputs to the CNN. The CNN comprises convolutional layers and dense layers for determining image features and increasing image resolution. The CNN is trained using backpropagation to adjust model weights and biases. Each convolutional layer of a CNN detects features in an image by comparing image subregions to a set of known kernels and determining similarities between subregions and kernels using a convolution operation. The dense layers of the CNN have full connections to all of the outputs of a previous layer to determine the specific target output result such as output image pixel values.