Abstract: In one embodiment, a method includes accessing an input image associated with an initial illumination, extracting from the input image at least a first, a second, and a third mono-channel images based on a first, a second, a third image channel, respectively, determining a first, a second, and a third correction for the first, second, and third mono-channel images with a first, a second, and a third correction algorithm, respectively, generating a first, a second, and a third corrected mono-channel image by applying the first, second, and third correction to the first, second, and third mono-channel image, respectively, and generating an output corrected image corresponding to the input image based on the first, second, and third mono-channel corrected images, wherein the output corrected image is associated with a corrected illumination with respect to the initial illumination.

Abstract: In one embodiment, a method of training a machine-learning model for modifying a facial illumination in an image includes accessing an initial image including a human face having an initial illumination and determining one or more illumination priors for the initial image. The method includes providing the initial image and the one or more illumination priors to the machine-learning model; receiving, from the machine-learning model, a set of correction operators identifying a modified illumination for the human face; creating, based at least on the set of correction operators and the initial image, a modified image having the modified illumination; creating, based on the modified image, a reconstructed initial image including the human face having a reconstructed illumination; and adjusting one or more parameters of the machine-learning model by minimizing a loss function based on a difference between the initial and the reconstructed initial images in their respective illumination.

Abstract: Image de-skewing can include acquiring pixel coordinates of an image captured by a camera adjoining an electronic display device. The pixel coordinates can be mapped to de-skewing coordinates using a predetermined de-skewing transformer that maps the pixel coordinates to de-skewing coordinates. A de-skewed image can be generated based on the de-skewing coordinates such that the de-skewed image is perpendicular to a principal axis extending from a predetermined location of an apparent camera on the electronic display device.

Abstract: A method includes capturing, by a camera disposed behind a display panel of an electronic device, an original image through a semi-transparent pixel region of the display panel. The original image is associated with one or more predetermined point spread functions (PSFs) and includes one or more blurring artifacts. The method further includes estimating an optimal regularization parameter for the original image based on the one or more predetermined PSFs and the one or more blurring artifacts, applying a self-regularizing inverse filter, such as a fast self-regularizing inverse filter, to the original image based on the estimated regularization parameter, and generating a deblurred image based on the self-regularizing inverse filter. Generating the deblurred image includes reconstructing the original image utilizing the self-regularizing inverse filter to remove the one or more blurring artifacts.

Abstract: A method includes accessing a display structure for manufacturing a display panel for an under-display camera (UDC) system. The method further includes computing a point spread function (PSF) of the UDC system with the display structure and computing a metric to evaluate performance of the display structure based on the PSF. The method further includes determining that the metric does not satisfy selection criteria for using the display structure for manufacturing the display panel for the UDC system and in response to the determination, iteratively optimizing the display structure, using an automated searching process, until the metric satisfies the selection criteria. The method further includes generating an optimized display structure responsive to completion of the automated searching process. The optimized display structure is used for manufacturing the display panel for the UDC system.

Abstract: A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.

Abstract: A method includes obtaining, using at least one processor, first and second input image frames, where the first and second input image frames are associated with first and second image planes, respectively. The method also includes obtaining, using the at least one processor, a depth map associated with the first input image frame. The method further includes producing another version of the depth map by performing one or more times: (a) projecting, using the at least one processor, the first input image frame to the second image plane in order to produce a projected image frame using (i) the depth map and (ii) information identifying a conversion from the first image plane to the second image plane and (b) adjusting, using the at least one processor, at least one of the depth map and the information identifying the conversion from the first image plane to the second image plane.