Enhancing the resolution of a video stream

1. A method comprising, by a computing device:

accessing first-resolution images corresponding to frames of a video;

computing a motion vector based on a first-resolution image of a first frame in the video and a first-resolution image of a second frame in the video;

generating a second-resolution warped image associated with the second frame by using the motion vector to warp a second-resolution reconstructed image associated with the first frame;

up-sampling the first-resolution image of the second frame using an interpolation technique to generate an up-sampled second-resolution image associated with the second frame;

computing adjustment parameters by processing the first-resolution image associated with the second frame and the second-resolution warped image associated with the second frame using a machine-learning model; and

adjusting pixels of the up-sampled second-resolution image associated with the second frame based on the adjustment parameters to reconstruct a second-resolution reconstructed image associated with the second frame.

2. The method of claim 1 , wherein differences between the second-resolution reconstructed image associated with the second frame and a second-resolution ground truth image associated with the second frame are measured during a training process of the machine-learning model.

3. The method of claim 2 , wherein gradients of trainable variables of the machine-learning model are computed based on the measured differences between the second-resolution reconstructed image associated with the second frame and the second-resolution ground truth image associated with the second frame.

4. The method of claim 3 , wherein the trainable variables are updated by a gradient-descent backpropagation procedure.

5. The method of claim 1 , wherein the first frame locates before the second frame in a frame sequence of the video.

6. The method of claim 1 , wherein differences between selected pixels in a second-resolution warped image associated with a third frame and the selected pixels in a second-resolution ground truth image associated with the third frame are measured during a training process of the machine-learning model.

7. The method of claim 6 , wherein the second frame locates before the third frame in a frame sequence of the video.

8. The method of claim 7 , wherein the selected pixels are identified as pixels with strong optical flow correspondence by comparing pixels in a warped second-resolution ground truth image associated with the second frame and a warped second-resolution ground truth image associated with the third frame.

9. The method of claim 7 , wherein the second-resolution warped image associated with the third frame is generated by:

computing a second motion vector based on the second-resolution ground truth image associated with the second frame and the second-resolution ground truth image associated with the third frame; and

generating the second-resolution warped image associated with the third frame by using the second motion vector to warp the second-resolution reconstructed image associated with the second frame.

10. The method of claim 6 , wherein gradients of trainable variables of the machine-learning model are computed based on the measured differences between the selected pixels in the second-resolution warped image associated with the third frame and the selected pixels in the second-resolution ground truth image associated with the third frame.

11. The method of claim 10 , wherein the trainable variables are updated by a gradient-descent backpropagation procedure.

12. The method of claim 1 , wherein a second resolution is higher than a first resolution.

13. The method of claim 12 , wherein generating the up-sampled second-resolution image associated with the second frame comprises:

uniformly placing pixels of the first-resolution image of the second frame into a second-resolution image plane for the up-sampled second-resolution image such that a plurality of pixels left blank; and

filling the plurality of blank pixels in the second-resolution image plane with interpolated values of non-blank neighboring pixels.

14. The method of claim 1 , wherein the second-resolution warped image associated with the second frame comprises objects located at predicted locations based on the computed motion vector.

15. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

access first-resolution images corresponding to frames of a video;

compute a motion vector based on a first-resolution image of a first frame in the video and a first-resolution image of a second frame in the video;

generate a second-resolution warped image associated with the second frame by using the motion vector to warp a second-resolution reconstructed image associated with the first frame;

up-sample the first-resolution image of the second frame using an interpolation technique to generate an up-sampled second-resolution image associated with the second frame;

compute adjustment parameters by processing the first-resolution image associated with the second frame and the second-resolution warped image associated with the second frame using a machine-learning model; and

adjust pixels of the up-sampled second-resolution image associated with the second frame based on the adjustment parameters to reconstruct a second-resolution reconstructed image associated with the second frame.

16. The media of claim 15 , wherein differences between the second-resolution reconstructed image associated with the second frame and a second-resolution ground truth image associated with the second frame are measured during a training process of the machine-learning model.

17. The media of claim 16 , wherein gradients of trainable variables of the machine-learning model are computed based on the measured differences between the second-resolution reconstructed image associated with the second frame and the second-resolution ground truth image associated with the second frame.

18. The media of claim 17 , wherein the trainable variables are updated by a gradient-descent backpropagation procedure.

19. The media of claim 15 , wherein the first frame locates before the second frame in a frame sequence of the video.

20. A system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:

access first-resolution images corresponding to frames of a video;

compute a motion vector based on a first-resolution image of a first frame in the video and a first-resolution image of a second frame in the video;

generate a second-resolution warped image associated with the second frame by using the motion vector to warp a second-resolution reconstructed image associated with the first frame;

up-sample the first-resolution image of the second frame using an interpolation technique to generate an up-sampled second-resolution image associated with the second frame;

compute adjustment parameters by processing the first-resolution image associated with the second frame and the second-resolution warped image associated with the second frame using a machine-learning model; and

adjust pixels of the up-sampled second-resolution image associated with the second frame based on the adjustment parameters to reconstruct a second-resolution reconstructed image associated with the second frame.