Learning depth super-resolution by using multi-scale convolutional neural network

Imaging techniques are the most rapidly growing area of computer vision, and the resolution has reached a new level. Super-resolution is a technique that enhances the resolution of images from the low-resolution input and help to accurately analyze and derive the data. Recently convolutional neural network are becoming mainstream in computer vision. Most existing CNN models based super-resolution either directly reconstruct the low-resolution input and then improve the resolution at the last layer, or another way is, to firstly enlarge the low-resolution input to high resolution (HR), then reconstruct the HR to obtain the desired output. These models encounter some major flows; large computational resources and losing information. In this paper, we adopt gradual process for training the CNN, to propose an efficient super-resolution model. The gradual strategy helps network to progressively magnify and reconstruct the LR image in each step, and thereby possibly avoid of losing information (second problem). In addition, we optimize the number of layers, add the residual network and skip connection to the proposed network to ease the difficulty of training (first problem). The proposed model not only achieves a compatible performance with the existing prominent methods but also, efficiently reduce the computational expenses.