Abstract: The embodiments described herein perform image infusion on Bayer image data that is coordinated with Clear image data captured with a dual camera. A first image sensor of the dual camera captures a Bayer image, while a second sensor captures a Clear image, where the first sensor and the second sensor are synchronized to capture the images at the same time. Some embodiments generate a relational mapping of Clear luma channel data extracted from the Clear image to Bayer luma channel data extracted from the Bayer image data, and use the relational mapping to generate multiple de-noised luma representations associated with the Bayer image. The multiple de-noised luma representations are then selectively merged together to generate a resultant image that retains a high signal-to-noise radio (SNR) and reduces artifacts obtained by the image processing.

Abstract: Gathering a plurality of sequential image sets from a scene. The sets include a main sequential image set and one or more additional sequential image sets. The main sequential image set includes a main central image that is near a temporal midpoint of the set, and one or more remaining main images. The additional sequential image sets each include a plurality of additional images comprising an additional central image and one or more additional remaining images. The one or more remaining main images and the plurality of additional images are used to perform filtering of the main central image to provide a filtered main central image having an enhanced signal-to-noise ratio.

Abstract: A method for processing digital media is described. In one example embodiment, the method may include detecting an unknown object in a video frame, receiving inputs representing probable identities of the unknown object in the video frame from various sources, and associating each input with the unknown object detected in the video frame. The received inputs may be processed, compared with reference data and, based on the comparison, probable identities of the object associated with the input derived. The method may further include retrieving a likelihood of the input to match the unknown object from historical data and producing weights corresponding to the inputs, fusing the inputs and the relative weight associated with each input, and identifying the unknown object based on a comparison of the weighted distances from the unknown identify to a reference identity.

Abstract: A method for processing digital media is described. In one example embodiment, the method may include detecting an unknown object in a video frame, receiving inputs representing probable identities of the unknown object in the video frame from various sources, and associating each input with the unknown object detected in the video frame. The received inputs may be processed, compared with reference data and, based on the comparison, probable identities of the object associated with the input derived. The method may further include retrieving a likelihood of the input to match the unknown object from historical data and producing weights corresponding to the inputs, fusing the inputs and the relative weight associated with each input, and identifying the unknown object based on a comparison of the weighted distances from the unknown identify to a reference identity.