Amazon Technologies, Inc.

Silviu Stefan Andrei

Walterio Wolfgang Mayol Cuevas

Nataliya Shapovalova

Wasiq Bokhari

Tejas Shamrao Khot

Techniques for enhancing an image are described. For example, a lower-resolution image from a video file may be enhanced using a trained neural network applying the trained neural network on the lower-resolution image to remove artifacts by removing artifacts by generating, using a layer of the trained neural network, a residual value based on the proper subset of the received image and at least one corresponding image portion of a preceding lower resolution image in the video file and at least one corresponding image portion of a subsequent lower resolution image in the video file, upscale the lower-resolution image using bilinear upsampling, and combine the upscaled received image and residual value to generate an enhanced image.

Video enhancement using a neural network

An input video stream corresponding to input video content may be received over one or more networks. The input video stream may include a first image frame. An input visual feature of the first image frame may be matched to a reference visual feature of a reference image. The reference image may have a higher image quality than the first image frame. A replacement visual feature may be generated for the input visual feature. The replacement visual feature may be generated based at least in part on the reference visual feature. An enhanced image frame corresponding to the first image frame may be generated by at least replacing the input visual feature in the first image frame with the replacement visual feature. An enhanced video stream may be provided. The enhanced image frame may be a substitute for the first image frame in the enhanced video stream.

Reference-based streaming video enhancement

Techniques for enhancing an image are described. For example, a lower-resolution image from a video file may be enhanced using a trained neural network applying the trained neural network on the lower-resolution image to remove artifacts by generating, using a layer of the trained neural network, a residual value based on the proper subset of the received image and at least one corresponding image portion of a previously generated higher resolution image in the video file, upscaling the received image using bilinear upsampling, and combining the upscaled received image and residual value to generate an enhanced image.

Video enhancement using a recurrent image date of a neural network

Techniques for enhancing an image are described. For example, a lower-resolution image, for example from a video file, may be enhanced using a trained neural network by applying the trained neural network to enhance a middle lower-resolution image of a plurality of lower-resolution images using a generator with filters of a generative adversary network. In some examples, a plurality of sequential feature processing acts are performed on the lower-resolution images to generate a residual which is added to a filtered version of one of the lower-resolution images to generate an enhanced image.

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Kirkland, WA, United States of America

Silviu Stefan Andrei