Abstract: A method includes obtaining, using at least one processing device of an electronic device, multiple image frames comprising a reference frame and multiple non-reference frames. The method also includes obtaining, using the at least one processing device, multiple motion maps, where each motion map is based on a comparison between the reference frame and a respective one of the non-reference frames. The method further includes generating, using the at least one processing device, multiple modified non-reference frames based on the motion maps. Generating the modified non-reference frames includes, for each non-reference frame, blending the non-reference frame and the reference frame based on the corresponding motion map associated with the non-reference frame. In addition, the method includes providing, using the at least one processing device, the reference frame and the modified non-reference frames to a blending network to generate a single image.

Abstract: A method includes training, using at least one processing device of an electronic device, a student variational autoencoder (VAE) based on a teacher VAE. Training the student VAE based on the teacher VAE includes using a first optimizer and a second optimizer. The first optimizer is configured to align a latent space of the student VAE with a latent space of the teacher VAE. The second optimizer is configured to optimize reconstruction performance of the student VAE. During use of the first optimizer, parameters of the teacher VAE may be frozen, and parameters of an encoder of the student VAE may be adjusted. During use of the second optimizer, the parameters of the encoder of the student VAE may be frozen, and parameters of a decoder of the student VAE may be adjusted.

Abstract: A method includes selecting a reference frame and a non-reference frame from a plurality of image frames. The method also includes generating a reference edge map identifying edges in the reference frame and a non-reference edge map identifying edges in the non-reference frame. The method further includes generating a moving edge map based on one or more movements between one or more of the edges of the reference edge map and one or more of the edges of the non-reference edge map. The method also includes generating a blend map based on the reference and non-reference frames. The method further includes modifying the blend map based on one or more indications of movement of corresponding pixels in the moving edge map to generate a modified blend map. In addition, the method includes blending the reference and non-reference frames based on the modified blend map to generate an output image.

Abstract: A method includes obtaining an under-display camera (UDC) image captured using a camera located under a display. The method also includes processing, using at least one processing device of an electronic device, the UDC image based on a machine learning model to restore the UDC image. The method further includes displaying or storing the restored image corresponding to the UDC image. The machine learning model is trained using (i) a ground truth image and (ii) a synthetic image generated using the ground truth image and a point spread function that is based on an optical transmission model of the display.

Abstract: A method includes identifying, using at least one processing device of an electronic device, a spatially-variant point spread function associated with an under-display camera. The spatially-variant point spread function is based on an optical transmission model and a layout of a display associated with the under-display camera. The method also includes generating, using the at least one processing device, a ground truth image. The method further includes performing, using the at least one processing device, a convolution of the ground truth image based on the spatially-variant point spread function in order to generate a synthetic sensor image. The synthetic sensor image represents a simulated image captured by the under-display camera. In addition, the method includes providing, using the at least one processing device, the synthetic sensor image and the ground truth image as an image pair to train a machine learning model to perform under-display camera point spread function inversion.

Abstract: A method includes obtaining an under-display camera (UDC) image captured using a camera located under a display. The method also includes processing, using at least one processing device of an electronic device, the UDC image based on a machine learning model to restore the UDC image. The method further includes displaying or storing the restored image corresponding to the UDC image. The machine learning model is trained using (i) a ground truth image and (ii) a synthetic image generated using the ground truth image and a point spread function that is based on an optical transmission model of the display.

Abstract: A method includes obtaining an under-display camera (UDC) image captured using a camera located under a display. The method also includes processing, using at least one processing device of an electronic device, the UDC image based on a machine learning model to restore the UDC image. The method further includes displaying or storing the restored image corresponding to the UDC image. The machine learning model is trained using (i) a ground truth image and (ii) a synthetic image generated using the ground truth image and a point spread function that is based on an optical transmission model of the display.

Abstract: A method includes identifying, using at least one processing device of an electronic device, a spatially-variant point spread function associated with an under-display camera. The spatially-variant point spread function is based on an optical transmission model and a layout of a display associated with the under-display camera. The method also includes generating, using the at least one processing device, a ground truth image. The method further includes performing, using the at least one processing device, a convolution of the ground truth image based on the spatially-variant point spread function in order to generate a synthetic sensor image. The synthetic sensor image represents a simulated image captured by the under-display camera. In addition, the method includes providing, using the at least one processing device, the synthetic sensor image and the ground truth image as an image pair to train a machine learning model to perform under-display camera point spread function inversion.