Depth map super-resolution reconstruction method based on convolutional neural networks

The invention belongs to the field of image processing, aims at restoring a high-resolution depth image and utilizing the great learning capacity of convolutional neural networks to solve the defects that the conventional algorithm is high in computational complexity and high in actual application cost and cannot effectively extract features, and provides the technical scheme of a depth map super-resolution reconstruction method based on the convolutional neural networks. The convolutional neural networks (CNN) combining a convolutional layer and a deconvolutional layer is utilized to extract the depth image features of low-resolution sample depth image block and a high-resolution sample depth image block, and then the nonlinear mapping relation between the depth image features is learnt so as to restore the high-resolution depth image. The depth map super-resolution reconstruction method based on the convolutional neural networks is mainly applied to the occasion of image processing.