Samsung Electronics Co., Ltd.

Yibo Xu

Hamid Rahim Sheikh

John W Glotzbach

Zhen Tong

John Seokjun Lee

Long N Le

Weidi Liu

Ruiwen Zhen

Baekdu Choi

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.

Machine learning model training using synthetic data for under-display camera (UDC) image restoration

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.

A method includes obtaining a blended red-green-blue (RGB) image frame of a scene. The method also includes performing, using at least one processing device of an electronic device, an interband denoising operation to remove at least one of noise and one or more artifacts from the blended RGB image frame in order to produce a denoised RGB image frame. Performing the interband denoising operation includes performing filtering of red, green, and blue color channels of the blended RGB image frame to remove at least one of the noise and the one or more artifacts from the blended RGB image frame. The filtering of the red and blue color channels of the blended RGB image frame is based on image data of at least one of the green color channel and a white color channel of the blended RGB image frame.

Apparatus and method for interband denoising and sharpening of images

A method includes obtaining multiple input image frames and determining how to warp at least one of the input image frames. The method also includes performing an intraband demosaic-warp operation to reconstruct image data in different color channels of the input image frames and warp the at least one input image frame to produce RGB input image frames. The method further includes blending the RGB input image frames to produce a blended RGB image frame, performing an interband denoising operation to produce a denoised RGB image frame, and performing an interband sharpening operation to produce a sharpened RGB image frame. In addition, the method includes performing an interband demosaic operation to substantially equalize high-frequency content in different color channels of the sharpened RGB image frame to produce an equalized sharpened RGB image frame and generating a final image of the scene based on the equalized sharpened RGB image frame.

Apparatus and method for combined intraband and interband multi-frame demosaicing

A method includes obtaining an image, where the image includes one or more frames. The method also includes performing multi-frame processing, image signal processing, and image processing on the image to produce a processed image. The method further includes applying a chroma suppression to the processed image. The chroma suppression is configured to reduce brightness and color saturation in one or more select areas within the processed image.

System and method to improve quality in under-display camera system with radially-increasing distortion

A method includes obtaining an image, where the image includes one or more frames. The method also includes performing multi-frame processing and image signal processing on the image to produce a processed image. The method further includes performing image restoration on the processed image to produce an output image. Performing the image restoration includes generating a dither signal, applying a dither signal corner attenuation to the dither signal in order to generate an attenuated dither signal, combining the attenuated dither signal with a sharpened denoised signal in order to generate a combine signal, and applying a halo suppression on the combined signal.

A system and method are provided to improve quality in an under-display camera (UDC) system with radially-increasing distortion. At least one processor receives an image and performs multi-frame processing, image signal processing, and a point spread function inversion (PSFI) on the image to produce a processed image. A PSFI radial coring is applied on the processed image to reduce noise in the processed image resulting from the PSFI. The at least one processor can apply a chroma suppression to the processing of the image to reduce brightness and color saturation is select areas in the processed image. Image restoration can be performed on the processed image to produce an output image. The image restoration may include generating a dither signal, applying a dither signal corner attenuation to the dither signal, combining the attenuated dither signal with a sharpened denoised signal, and applying a halo suppression on the combined signals.

System and method to improve quality in under-display camera system with radially increasing distortion

Various image sharpening techniques are disclosed. For example, a method for image sharpening includes obtaining, using at least one sensor of an electronic device, an image that includes visual content. The method also includes generating an edge map that indicates edges of the visual content within the image. The method further includes applying a high-pass signal and an adaptive gain based on the edge map to sharpen the image. The method also includes generating a bright halo mask and a dark halo mask based on the edge map, where the bright halo mask indicates an upper sharpening limit and the dark halo mask indicates a lower sharpening limit. In addition, the method includes modifying a level of sharpening at one or more of the edges within the sharpened image to provide halo artifact reduction based on the bright halo mask and the dark halo mask.

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Plano, TX, United States of America

Yibo Xu