Multi-stage multi-reference bootstrapping for video super-resolution

1. A computer-implemented method, comprising:

obtaining a current frame and a plurality of reference frames of the current frame in a video;

constructing a first tensor, wherein the first tensor is a first feature map constructed using the current frame and the plurality of reference frames;

performing first convolutional filtering on the first tensor, to generate a first estimated image of the current frame;

obtaining estimated reference images of the plurality of reference frames, the estimated reference images having a higher resolution than images of the plurality of reference frames; and

performing second convolutional filtering on a second tensor constructed using the first estimated image of the current frame and the estimated reference images of the plurality of reference frames, to generate a second estimated image of the current frame.

2. The computer-implemented method of claim 1 , wherein the plurality of reference frames of the current frame comprises preceding frames of the current frame.

3. The computer-implemented method of claim 1 , wherein the plurality of reference frames of the current frame comprises frames preceding the current frame and frames subsequent to the current frame.

4. The computer-implemented method of claim 1 , the constructing the first tensor comprising:

before the performing the first convolutional filtering:

determining an expansion region in a reference frame of the plurality of reference frames, the expansion region corresponding to a region in the current frame, and the expansion region in the reference frame comprising an enlarged scene of the region in the current frame;

assigning a utility score to each pixel of the reference frame based on whether or not each pixel of the reference frame belongs to the expansion region, thereby generating a utility mask of the reference frame, the utility mask comprising a set of utility scores for pixels of the reference frame; and

constructing the first tensor using the current frame, the plurality of reference frames, and the utility mask of the reference frame.

5. The computer-implemented method of claim 4 , further comprising:

generating a scene flow using the current frame and the plurality of reference frames, the scene flow comprising images of the plurality of reference frames that are motion compensated based on an image of the current frame; and

generating a flow map for each of the plurality of reference frames,

wherein the determining the expansion region in the reference frame is based on the scene flow and the flow map.

6. The computer-implemented method of claim 5 , wherein the generating the scene flow comprises:

generating the scene flow using the current frame, the plurality of reference frames, and a digital world reference image of the current frame.

7. The computer-implemented method of claim 6 , wherein the digital world reference image is obtained from a digital world image database.

8. The computer-implemented method of claim 7 , further comprising:

obtaining visual positioning system (VPS) information of the current frame; and

searching for the digital world reference image in the digital world image database according to the VPS information.

9. The computer-implemented method of claim 6 , further comprising:

resizing the digital world reference image so that the digital world reference image has a same size as the image of the current frame.

10. The computer-implemented method of claim 4 , further comprising:

generating a corresponding utility mask for each of the plurality of reference frames; and

constructing the first tensor using the current frame, the plurality of reference frames and utility masks of the plurality of reference frames.

11. The computer-implemented method of claim 10 , wherein the constructing the first tensor comprises:

ordering the current frame and the plurality of reference frames according to a sequence of the current frame and the plurality of reference frames; and

ordering the utility masks of the plurality of reference frames according to the sequence.

12. The computer-implemented method of claim 10 , wherein the constructing the first tensor comprises:

multiplying values of pixels of each of the plurality of reference frames and the corresponding utility mask of each of the plurality of reference frames.

13. The computer-implemented method of claim 1 , wherein the first estimated image or the second estimated image has a larger size than an image of the current frame.

14. The computer-implemented method claim 1 , further comprising:

before the performing the second convolutional filtering:

determining an expansion region in an estimated reference image of the plurality of reference frames, the expansion region corresponding to a region in the first estimated image, and the expansion region in the estimated reference image comprising an enlarged scene of the region in the first estimated image;

assigning a utility score to each pixel of the estimated reference image based on whether or not each pixel of the estimated reference image belongs to the expansion region, thereby generating a utility mask of the estimated reference image, the utility mask of the estimated reference image comprising a set of utility scores for pixels of the estimated reference image; and

constructing the second tensor using the first estimated image, the estimated reference images of the plurality of reference frames and the utility mask of the estimated reference image.

15. The computer-implemented method of claim 14 , further comprising:

generating a scene flow using the first estimated image and the estimated reference images of the plurality of reference frames, the scene flow comprising images of the estimated reference images that are motion compensated based on the first estimated image; and

generating a flow map for each of the estimated reference images; and

wherein the determining the expansion region in the estimated reference image is based on the scene flow and the flow map.

16. The computer-implemented method of claim 14 , further comprising:

generating a respective utility mask for each of the estimated reference images.

17. The computer-implemented method of claim 16 , further comprising:

constructing the second tensor using the first estimated image, the estimated reference images of the plurality of reference frames and utility masks of the estimated reference images.

18. The computer-implemented method of claim 17 , further comprising:

performing convolutional filtering on the estimated first image and the estimated reference images of the plurality of reference frames, thereby generating a second feature map of the estimated first image and the estimated reference images,

wherein the constructing the second tensor comprises:

constructing the second tensor using the second feature map, the utility masks of the estimated reference images, and the first tensor.

19. A computer-implemented method, comprising:

obtaining a current frame and a plurality of reference frames of the current frame in a video;

determining an expansion region in a reference frame of the plurality of reference frames, the expansion region corresponding to a region in the current frame, and the expansion region in the reference frame comprising an enlarged scene relative to the region in the current frame;

assigning a utility score to each pixel of the reference frame based on whether or not each pixel of the reference frame belongs to the expansion region, thereby generating a utility mask of the reference frame, the utility mask comprising a set of utility scores for pixels of the reference frame indicating which pixels are in the expansion region in the reference frame;

constructing a first tensor using the current frame, the plurality of reference frames and the utility mask of the reference frame; and

performing convolutional filtering on the first tensor to generate a first reconstructed image of the current frame.

20. The computer-implemented method of claim 19 , further comprising:

generating a scene flow using the current frame and the plurality of reference frames, the scene flow comprising images of the plurality of reference frames that are motion compensated based on an image of the current frame; and

generating a flow map for each of the plurality of reference frames,

wherein the determining the expansion region in the reference frame is based on the scene flow and the flow map.

21. The computer-implemented method of claim 20 , wherein the generating the scene flow comprises:

generating the scene flow using the current frame, the plurality of reference frames, and a digital world reference image of the current frame.

22. The computer-implemented method of claim 21 , wherein the digital world reference image is obtained from a digital world image database.

23. The computer-implemented method of claim 22 , further comprising:

obtaining visual positioning system (VPS) information of the current frame; and

searching for the digital world reference image in the digital world image database according to the VPS information.

24. The computer-implemented method of claim 21 , further comprising:

resizing the digital world reference image so that the digital world reference image has a same size as the image of the current frame.

25. The computer-implemented method of claim 19 , further comprising:

generating a corresponding utility mask for each of the plurality of reference frames; and

constructing the first tensor using the current frame, the plurality of reference frames and utility masks of the plurality of reference frames.

26. The computer-implemented method of claim 25 , wherein the constructing the first tensor comprising:

ordering the current frame and the plurality of reference frames according to a sequence of the current frame and the plurality of reference frames; and

ordering the utility masks of the plurality of reference frames according to the sequence.

27. The computer-implemented method of claim 26 , wherein the constructing the first tensor comprising:

multiplying values of pixels of each of the plurality of reference frames and the corresponding utility mask of each of the plurality of reference frames.

28. An apparatus comprising:

a non-transitory memory storage comprising instructions; and

one or more processors in communication with the non-transitory memory storage, wherein the one or more processors execute the instructions to cause the apparatus to perform operations including:

obtaining a current frame and a plurality of reference frames of the current frame in a video;

constructing a first tensor, wherein the first tensor is a first feature map constructed using the current frame and the plurality of reference frames;

performing first convolutional filtering on the first tensor, to generate a first estimated image of the current frame;

obtaining estimated reference images of the plurality of reference frames, the estimated reference images having a higher resolution than images of the plurality of reference frames; and

performing second convolutional filtering on a second tensor constructed using the first estimated image of the current frame and the estimated reference images of the plurality of reference frames, to generate a second estimated image of the current frame.