Video resolution enhancement method, storage medium, and electronic device

A non-transitory computer-readable storage medium on which a computer program is stored, wherein the program, when executed by a processor, implements a method for enhancing a video resolution comprising:

• obtaining multiple frames of images as input data, and obtaining initial data by performing feature extraction on the input data using a first three-dimensional convolutional layer;
• obtaining first feature data by performing down-sampling on the initial data at a preset multiple;
• obtaining first reference data by performing a convolution operation on the first feature data using a second three-dimensional convolutional layer to merge the first feature data into one frame;
• and
• obtaining first output data by performing up-sampling on the first reference data at the preset multiple;
• wherein the method further comprises:
• performing an Nth super-resolution operation on the first feature data, the super-resolution operation comprising a down-sampling operation, a first feature extraction operation, a merging operation, a second feature extraction operation, and an up-sampling operation;
• wherein
• the down-sampling operation comprises performing down-sampling on the first feature data at the preset multiple;
• the first feature extraction operation comprises performing the first feature extraction operation on the down-sampled first feature data by using the first three-dimensional convolution layer to obtain third feature data;
• the merging operation comprises performing a convolution operation on the third feature data by using the second three-dimensional convolutional layer to merge the third feature data into one frame to obtain second reference data;
• the second feature extraction operation comprises performing the second feature extraction operation on stacked data of the second reference data and (N+1)th output result by using the first three-dimensional convolution layer to obtain fourth feature data;
• and
• the up-sampling operation comprises performing up-sampling on the fourth feature data at the preset multiple to obtain third output data;
• and
• updating the first reference data with the third output data;
• wherein an input of Nth down-sampling operation is an output of the first feature extraction operation of (N−1)th super-resolution operation, and N is a positive integer starting from 1.

An electronic device, comprising:

• one or more processors;
• and
• a memory configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to:
• obtain multiple frames of images as input data, and obtain initial data by performing feature extraction on the input data using a first three-dimensional convolutional layer;
• obtain first feature data by performing down-sampling on the initial data at a preset multiple;
• obtain first reference data by performing a convolution operation on the first feature data using a second three-dimensional convolutional layer to merge the first feature data into one frame;
• and
• obtain first output data by performing up-sampling on the first reference data at the preset multiple;
• wherein the one or more processors are further configured to:
• perform an Nth super-resolution operation on the first feature data, the super-resolution operation comprising a down-sampling operation, a first feature extraction operation, a merging operation, a second feature extraction operation, and an up-sampling operation;
• wherein
• the down-sampling operation comprises performing down-sampling on the first feature data at the preset multiple;
• the first feature extraction operation comprises performing the first feature extraction operation on the down-sampled first feature data by using the first three-dimensional convolution layer to obtain third feature data;
• the merging operation comprises performing a convolution operation on the third feature data by using the second three-dimensional convolutional layer to merge the third feature data into one frame to obtain second reference data;
• the second feature extraction operation comprises performing the second feature extraction operation on stacked data of the second reference data and (N+1)th output result by using the first three-dimensional convolution layer to obtain fourth feature data;
• and
• the up-sampling operation comprises performing up-sampling on the fourth feature data at the preset multiple to obtain third output data;
• and
• update the first reference data with the third output data;
• wherein an input of Nth down-sampling operation is an output of the first feature extraction operation of (N−1)th super-resolution operation, and N is a positive integer starting from 1.