Abstract:
A method for switching between a first lens and a second lens in an electronic device includes displaying, by the electronic device, a first frame showing a field of view (FOV) of the first lens; detecting, by the electronic device, an event that causes the electronic device to transition from displaying the first frame to displaying a second frame showing a FOV of the second lens; generating, by the electronic device and based on the detecting the event, at least one intermediate frame for transitioning from the first frame to the second frame; and switching, by the electronic device and based on the detecting the event, from the first lens to the second lens and displaying the second frame, wherein the at least one intermediate frame is displayed after the displaying the first frame and before the displaying the second frame while the switching is performed.
Abstract:
Embodiments herein disclose a method for recommending an image capture mode by an electronic device. The method includes identifying, by the electronic device, at least one ROI displayed in a camera preview of the electronic device for capturing an image in a non-ultra-wide image capture mode. Further, the method includes determining, by the electronic device, that the at least one ROI is suitable to capture in an ultra-wide image capture mode. Further, the method includes providing, by the electronic device, at least one recommendation to switch to the ultra-wide image capture mode from the non-ultra-wide image capture mode for capturing the image.
Abstract:
A method for generating metadata pertaining to a RAW frame includes selecting an input frame from a captured RAW frame, a plurality of frames obtained by processing the captured RAW frame, and a scaled RAW frame, selecting identified salient regions in an output frame, constructed from the captured RAW frame, based on errors between regions of the input frame and a corresponding reconstruction of the region of the input frame from the identified salient regions in the output frame, obtaining a plurality of reconstructed frames, reconstructed from a plurality of blocks of each salient region, corresponding to a plurality of regions of the input frame, and generating metadata for reconstructing the captured RAW frame by encoding a plurality of errors between the plurality of reconstructed frames and corresponding plurality of regions of the input frame, and a reconstruction technique used for reconstructing the plurality of reconstructed frames.
Abstract:
Embodiments herein provide a method for removing an artefact in a high resolution image by an electronic device (100). The method includes receiving the high resolution image comprising the artefact. Further, the method includes downscaling the high resolution image into a plurality of lower resolution images. Further, the method includes removing the artefact from the plurality of lower resolution images by applying at least one first machine learning model from a plurality of machine learning models on the plurality of lower resolution images. Further, the method includes generating a high resolution image free from the artefact by applying at least one second machine learning model from the plurality of machine learning models on an output from the at least one first machine learning model. The output from each of the machine learning model comprises a low resolution image free from the artefact.
Abstract:
Example embodiments include a method and an electronic device for detecting and removing artifacts/degradations in media. Embodiments may detect artifacts and/or degradations in the media based on tag information indicating at least one artifact included in the media. The detection may be triggered automatically or manually. Embodiments may generate artifact/quality tag information associated with the media to indicate artifacts and/or degradations present in the media, and may store the artifact/quality tag information as metadata and/or in a database. Embodiments may identify, based on the artifact/quality tag information associated with the media, at least one artificial intelligence (AI)-based media processing model to be applied to the media to enhance the media. The at least one AI-based media processing model may be configured to enhance at least one artifact detected in the media. Embodiments may enhance the media by applying the at least one AI-based media enhancement model to the media.
Abstract:
A method for switching between a first lens and a second lens in an electronic device includes displaying, by the electronic device, a first frame showing a field of view (FOV) of the first lens; detecting, by the electronic device, an event that causes the electronic device to transition from displaying the first frame to displaying a second frame showing a FOV of the second lens; generating, by the electronic device and based on the detecting the event, at least one intermediate frame for transitioning from the first frame to the second frame; and switching, by the electronic device and based on the detecting the event, from the first lens to the second lens and displaying the second frame, wherein the at least one intermediate frame is displayed after the displaying the first frame and before the displaying the second frame while the switching is performed.
Abstract:
A method and an apparatus for generating a composite image in an electronic device are provided. The method includes identifying a first image element of a first event from first images successively captured by a first image sensor of the electronic device, and identifying a second image element of a second event from second images successively captured by a second image sensor of the electronic device, the first images and the second images being simultaneously captured. The method further includes combining the first image element with the second image element based on a synchronization parameter to generate the composite image.