Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: In some embodiments, an integrated virtual button module includes a first transducer, a microcontroller, and a first driver circuit. The first transducer includes a transient strain-sensing element and is configured to generate first signals. The microcontroller is configured to obtain first data from the first signals and determine user inputs in accordance with at least the first data. The first driver circuit is configured to receive user feedback data and to generate a first user feedback signal in accordance with the user feedback data. The first driver circuit is electronically couplable to a first actuator. The user feedback data are determined in accordance with at least the user inputs. The first actuator emits a haptic signal and/or a first audio signal when driven by the first user feedback signal. Embodiments of an integrated virtual button system and a method of determining user input and providing user feedback are also disclosed.

Abstract: In some embodiments, an integrated virtual button module includes a first transducer, a microcontroller, and a first driver circuit. The first transducer includes a transient strain-sensing element and is configured to generate first signals. The microcontroller is configured to obtain first data from the first signals and determine user inputs in accordance with at least the first data. The first driver circuit is configured to receive user feedback data and to generate a first user feedback signal in accordance with the user feedback data. The first driver circuit is electronically couplable to a first actuator. The user feedback data are determined in accordance with at least the user inputs. The first actuator emits a haptic signal and/or a first audio signal when driven by the first user feedback signal. Embodiments of an integrated virtual button system and a method of determining user input and providing user feedback are also disclosed.

Abstract: An electronic device comprises a display, an illumination source, a camera, and a logic system. The illumination source is configured to project structured illumination onto a subject. The camera is configured to image the subject through the display, which includes collecting the structured illumination as reflected by the subject. The logic system is configured to receive, from the camera, a digital image of the subject imaged through the display. The logic system is further configured to sharpen the digital image based on the spatially resolved intensity of the structured illumination as reflected by the subject.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Examples are disclosed that relate to the restoration of degraded images acquired via a behind-display camera. One example provides a method of training a machine learning model, the method comprising inputting training image pairs into the machine learning model, each training image pair comprising an undegraded image and a degraded image that represents an appearance of the undegraded image to a behind-display camera, and training the machine learning model using the training image pairs to generate frequency information that is missing from the degraded images.

Abstract: Methods, systems and computer program products are provided for a logo camera. Camera thickness may be reduced using multiple lenses and sensors. A fixed or variable color icon may provide a function, notice or privacy warning, convey information, and/or serve decorative and/or other purposes. Color filters may be fixed or variable. A color icon may be created, for example, by controlling the colors of display pixels aligned with optical paths of camera lens and sensor arrays. Colors in an icon may provide color filters corresponding to an array of lenses that focus color-separated light on one or more camera sensors. Sensors may be optimized for particular colors. Color-separated images may be combined into a single image. Lens and camera sensor arrays may be compatible with multiple logos or other icon images. Cameras behind fixed or variable color icons may be concealed, for example, using shutters or semi-reflective layers.