Abstract: Methods and systems for denoising. A method for denoising includes receiving an image from an image sensor, denoising the image, in a non-linear domain by a denoiser, by applying a noise map to the image to obtain a denoised image. Training losses for the denoiser are processed in a linear domain. The method includes storing, displaying, or transmitting an output image based on the denoised image.

Abstract: A video may include multiple video frames. The video frames may be scored based on values of multiple content metrics for individual frames. One or more portions of the video that includes a threshold number of consecutive video frames that meet a score threshold may be identified. For individual ones of the identified portion(s), a video frame may be selected based on a maximum of the score for presentation as an exemplar image.

Abstract: An image capture device may detect presence of an on-housing hand within a periphery of a field of view of an optical element during capture of visual content through the optical element. The image capture device may generate an alarm to indicate the presence of the on-housing hand within the periphery of the field of view of the optical element.

Abstract: Automated camera mode selection using local motion vectors is described. An image capture device includes an image metric estimation unit to detect a presence or level of an image metric in a scene of an image to capture based on image metric inputs, a motion estimation unit to determine a level of movement within the scene using ratios of histograms based on local motion vectors, a mode selection unit to select a camera mode for capturing the image based on outputs of the image metric estimation unit and the motion estimation unit, and an image sensor to capture the image according to the selected camera mode. The motion estimation unit computes a norm for each local motion vector, generates a histogram from the computed norms, determines movement metrics based on the ratios of histograms associated with different norm thresholds, and identifies the level of movement based on the movement metrics.

Abstract: Automated camera mode selection using local motion vectors is described. An image capture device includes an image metric estimation unit to detect a presence or level of an image metric in a scene of an image to capture based on image metric inputs, a motion estimation unit to determine a level of movement within the scene using ratios of histograms based on local motion vectors, a mode selection unit to select a camera mode for capturing the image based on outputs of the image metric estimation unit and the motion estimation unit, and an image sensor to capture the image according to the selected camera mode. The motion estimation unit computes a norm for each local motion vector, generates a histogram from the computed norms, determines movement metrics based on the ratios of histograms associated with different norm thresholds, and identifies the level of movement based on the movement metrics.

Abstract: A video may include multiple video frames. The video frames may be scored based on values of multiple content metrics for individual frames. One or more portions of the video that includes a threshold number of consecutive video frames that meet a score threshold may be identified. For individual ones of the identified portion(s), a video frame may be selected based on a maximum of the score for presentation as an exemplar image.

Abstract: A video may include multiple video frames. The video frames may be scored based on values of multiple content metrics for individual frames. One or more portions of the video that includes a threshold number of consecutive video frames that meet a score threshold may be identified. For individual ones of the identified portion(s), a video frame may be selected based on a maximum of the score for presentation as an exemplar image.