Adobe, Inc.

Aarsh Prakash Agarwal

Harshit Nyati

Shubhi Gupta

Shobhit Sinha

In implementations of systems for single image reflection removal, a computing device implements a removal system to receive data describing a digital image that depicts light reflected by a surface and light transmitted through the surface. The removal system predicts an edge map of a transmitted image for the light transmitted through the surface by processing the data using a first machine learning model trained on a first type of training data. A reflected component is predicted for the light reflected by the surface by processing the data using a second machine learning model trained on a second type of training data. A corrected digital image is generated that does not depict the light reflected by the surface based on the data, the edge map of the transmitted image, and the reflected component.

Systems for single image reflection removal

Embodiments are disclosed for deblurring motion in video. A method of deblurring motion in video can include receiving an input frame from a digital video, extracting a plurality of features of the input frame using an encoder network, determining, using a neural network, a plurality of spatial alignment kernels and a plurality of deblur kernels each corresponding to a feature of the input frame, wherein the plurality of spatial alignment kernels include different sizes of spatial alignment kernels and wherein the plurality of deblur kernels include different sizes of deblur kernels, generating, by the neural network, a plurality of output features for the input frame using the plurality of spatial alignment kernels and the plurality of deblur kernels, and generating a deblurred output frame from the plurality of output features using a decoder network.

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Lucknow, India

Aarsh Prakash Agarwal