Microsoft Technology Licensing, LLC

Luming Liang

Ilya Dmitriyevich Zharkov

Vivek Pradeep

Faezeh Amjadi

Tianyu Ding

Zhicheng Geng

A technique is described herein for temporally and spatially interpolating input video information, to produce output video information having a higher frame rate and a higher resolution compared to that exhibited by the input video information. The technique generates feature information based on plural frames of the input video information. The technique then produces the output video information based on the feature information using an architecture having, in order, a multi-stage encoding operation, a query-generating operation, and a multi-stage decoding operation. Each encoding stage produces an instance of encoder attention information that expresses identified relations across the plural frames of the input video information. Each decoding stage operates on an instance of encoder attention information produced by a corresponding encoding stage. The transformer architecture is compact and is capable of interpolating the input video information in real time.

Unified space-time interpolation of video information

Systems and methods for generating interpolated images are disclosed. In examples, image features are extracted from a first image and a second image; such image features may be warped using first and second plurality of parameters. A first candidate intermediate frame may be generated based on the warped first features and the warped second features. Multi-scale features associated with the image features extracted from the first image and the second image may be obtained and warped using the first and second plurality of parameters. A second candidate intermediate frame may be generated based on the warped first multi-scale features and the warped second multi-scale features. By blending the first candidate intermedia frame with the second candidate intermediate frame, an interpolated image may be generated.

Video frame interpolation via feature pyramid flows

A computational photography system is described herein including a guidance system and a detail enhancement system. The guidance system uses a first neural network that maps an original image provided by an image sensor to a guidance image, which represents a color-corrected and lighting-corrected version of the original image. A combination unit combines the original image and the guidance image to produce a combined image. A detail-enhancement system then uses a second neural network to map the combined image to a predicted image. The predicted image supplements the guidance provided by the first neural network by sharpening details in the original image. A training system is also described herein for training the first and second neural networks. The training system alternates in the data it feeds the second neural network, first using a guidance image as input to the second neural network, and then using a corresponding ground-truth image.

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Redmond, WA, United States of America

Luming Liang