Abstract: A computer-implemented method of configuring an electronic device for inpainting source three-dimensional (3D) scenes, includes: receiving the source 3D scenes and a user's input about a first object of the source 3D scenes; generating accurate object masks about the first object of the source 3D scenes; and generating inpainted 3D scenes of the source 3D scenes by using an inpainting neural radiance field (NeRF) based on the accurate object masks.

Abstract: 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.

Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for performing multi-frame de-fencing by a device. In some embodiments, a method includes obtaining an image burst having at least one portion of a background scene obstructed by an opaque obstruction. The method further includes generating a plurality of obstruction masks marking the at least one portion of the background scene obstructed by the opaque obstruction in images of the image burst. The method further includes computing a motion of the background scene, with respect to a keyframe selected from the plurality of images, by applying an occlusion-aware optical flow model. The method further includes reconstructing the selected keyframe by providing a combination of features to an image fusion and inpainting network. The method further includes providing, to the user, the reconstructed keyframe comprising an unobstructed version of the background scene of the image burst.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.