Multi-scale-factor image super resolution with micro-structured masks

An apparatus for image processing, the apparatus comprising:

• at least one memory configured to store computer program code;
• at least one processor configured to access the computer program code and operate as instructed by the computer program code, the computer program code including:
• obtaining code configured to cause the at least one processor to obtain an input low resolution (LR) image comprising a height, a width, and a number of channels;
• implementing code configured to cause the at least one processor to implement a feature learning deep neural network (DNN) configured to compute a feature tensor based on the input LR image;
• generating code configured to cause the at least one processor to generate, by an upscaling DNN, a high resolution (HR) image, having a higher resolution than the input LR image, based on the feature tensor computed by the feature learning DNN,
• wherein a networking structure of the upscaling DNN differs depending on different scale factors, and
• wherein a networking structure of the feature learning DNN is a same structure for each of the different scale factors.

A non-transitory computer readable medium storing a program causing a computer to execute a process, the process comprising:

• obtaining an input low resolution (LR) image comprising a height, a width, and a number of channels;
• implementing a feature learning deep neural network (DNN) configured to compute a feature tensor based on the input LR image;
• generating, by an upscaling DNN, a high resolution (HR) image, having a higher resolution than the input LR image, based on the feature tensor computed by the feature learning DNN,
• wherein a networking structure of the upscaling DNN differs depending on different scale factors, and
• wherein a networking structure of the feature learning DNN is a same structure for each of the different scale factors.