Samsung Electronics Co., Ltd.

Aleksai Levinshtein

Allan Jepson

Michael S Brown

Abdelrahman Abdelhamed

Abhijith Punnappurath

Mete Kemertas

Haotian Zhang

Stavros Tsogkas

Abdullah Abuolaim

Xinyu Sun

Hoda Rezaee Kaviani

Haicheng Wang

Vineeth Subrahmanya Bhaskara

A method for training a super-resolution network may include obtaining a low resolution image; generating, using a first machine learning model, a first high resolution image based on the low resolution image; generating, using a second machine learning model, a second high resolution image based on the first high resolution image and an unpaired dataset of high resolution images; obtaining a training data set using the low resolution image and the second high resolution image; and training the super-resolution network using the training data set.

Unsupervised super-resolution training data construction

Image alignment is achieved by obtaining a fused optical flow based on a global motion estimate and an optical flow estimate. The fused optical flow may be used to warp the first image and/or the second image into aligned features or aligned images. An output image may then be obtained by combining.

Semi-global neural image alignment

A method of generating a low-light image is provided. The method includes receiving a raw image, removing an amount of first illumination from the raw image, applying a low exposure condition to the raw image having the amount of first illumination removed, and applying an amount of low-light illumination to the raw image having the applied low exposure condition.

System and method for synthesizing low-light images

An apparatus for scrubbing an image may include a memory storing instructions; and a processor configured to execute the instructions to: receive an input image; input a preset public attribute to an encoder neural network; obtain a scrubbed feature from the input image based on the preset public attribute, via the encoder neural network; wherein the encoder neural network is trained based on an amount of information in the scrubbed feature about the input image, and an estimated public attribute estimated from the scrubbed feature.

Method and apparatus for data anonymization

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Toronto, Canada

Aleksai Levinshtein