Abstract: A method 300B includes detecting a user input indicative of a trigger to modify the artifact of the image displayed at a user interface of an electronic device 100. Furthermore, the method 300B includes determining an artifact modification parameter based on a characteristic of the user input. Furthermore, the method 300B includes modifying the artifact in the image based on the artifact modification parameter.

Abstract: An electronic device is provided. The electronic device includes a display, a camera module disposed under the display, and a processor electrically connected to the display and the camera module. The processor is configured to acquire a sample frame by using the camera module, identify whether a light source object is included in the sample frame, determine an imaging parameter for acquisition of first multiple frames when the light source object is identified to be included in the sample frame, acquire multiple frames, based on the imaging parameter, composite the multiple frames to generate a composite frame, identify an attribute of the light source object included in the composite frame, and perform frame correction of the composite frame, based on the identified attribute.

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: A method performed by an electronic device for generating a slow motion video includes obtaining plural image frames captured at a frame rate from a camera of the electronic device, detecting motion information in a first image frame from among the plural image frames, and performing an interpolation operation using one or more image frames from among the plural image frames. The one or more image frames are image frames obtained during a predetermined time after the first image frame in which the motion information is detected. A second image frame obtained before the first image frame in which the motion information is detected is discarded from the electronic device. A third image frame obtained after the predetermined time from the first image frame in which the motion information is detected is discarded from the electronic device.

Abstract: An electronic device includes a UDC and a UDC controller configured to determine an optimal number of frames required to be captured for a scene to compensate at least one parameter to optimize an output media using the UDC, obtain a multi-frame fusion media by performing at least one multi-frame fusion on the determined optimal number of frames, perform a light source spread correction on the multi-frame fusion media, and optimize the output media based on the light source spread correction on the multi-frame fusion media.

Abstract: A method performed by an electronic device for generating a slow motion video includes obtaining plural image frames captured at a frame rate from a camera of the electronic device, detecting motion information in a first image frame from among the plural image frames, and performing an interpolation operation using one or more image frames from among the plural image frames. The one or more image frames are image frames obtained during a predetermined time after the first image frame in which the motion information is detected. A second image frame obtained before the first image frame in which the motion information is detected is discarded from the electronic device. A third image frame obtained after the predetermined time from the first image frame in which the motion information is detected is discarded from the electronic device.

Abstract: A method and an apparatus for capturing a high quality dynamic image by setting a different row-wise exposure value when capturing a scene are provided. The dynamic image capturing method includes: generating an image by pre-capturing a scene via an event sensor; generating event data from the image; determining a row-wise exposure value of the image based on the event data; and determining a row-wise readout priority order of the image based on the row-wise exposure value of the image.

Abstract: A method for performing multi-functional image restoration by an electronic device with a trained Machine Learning (ML) model is provided. The method includes receiving an image and determining channels of the image, and determining whether a number of restructuring needed for the channels is one. When the restructuring needed for the channels not one, then the method includes restructuring each channel into a first channel set, generating first inferences of the image corresponding to each channel by feeding the first channel set to the trained ML model, and generating a final inference image by combining the first inferences. When the number of restructuring needed for the channels is one, then the method includes restructuring the channels into a second channel set, and generating a second inference of the image by feeding the second channel set to the trained ML model.

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 performed by an electronic device for generating a slow motion video includes obtaining plural image frames captured at a frame rate from a camera of the electronic device, detecting motion information in a first image frame from among the plural image frames, and performing an interpolation operation using one or more image frames from among the plural image frames. The one or more image frames are image frames obtained during a predetermined time after the first image frame in which the motion information is detected. A second image frame obtained before the first image frame in which the motion information is detected is discarded from the electronic device. A third image frame obtained after the predetermined time from the first image frame in which the motion information is detected is discarded from the electronic device.

Abstract: A method for processing image frames, and an electronic device are provided. The method includes receiving image frames captured by an image sensor at a first frame rate and storing the received image frames in a memory. A change is detected in the stored image frames and an image frame is interpolated between the stored image frames after detecting the change. The interpolated image is stored in the memory.

Abstract: Methods and systems for reconstructing a high frame rate high resolution video in a Bayer domain, when an imaging device is set in a Flexible Sub-Sampled Readout (FSR) mode are described. A method provides the FSR mode, which utilizes a multiparty FSR mechanism to spatially and temporally sample the full frame Bayer data. The multi parity FSR utilizes a zigzag sampling that assists reconstruction of motion compensated artifact free high frame rate high resolution video with full frame size. The method includes reconstructing the high frame rate high resolution video using the plurality of parity fields generated. The reconstruction is based on a FSR reconstruction mechanism that can be a pre-Image Signal Processor (ISP) FSR reconstruction or a post-ISP FSP reconstruction based on bandwidth capacity of an ISP used by the imaging device.

Abstract: Methods and systems for reconstructing a high frame rate high resolution video in a Bayer domain, when an imaging device is set in a Flexible Sub-Sampled Readout (FSR) mode are described. A method provides the FSR mode, which utilizes a multiparty FSR mechanism to spatially and temporally sample the full frame Bayer data. The multi parity FSR utilizes a zigzag sampling that assists reconstruction of motion compensated artifact free high frame rate high resolution video with full frame size. The method includes reconstructing the high frame rate high resolution video using the plurality of parity fields generated. The reconstruction is based on a FSR reconstruction mechanism that can be a pre-Image Signal Processor (ISP) FSR reconstruction or a post-ISP FSP reconstruction based on bandwidth capacity of an ISP used by the imaging device.

Abstract: Accordingly the embodiments herein provide a method for rendering a background in an image by an electronic device (100). The method includes recognizing, by the electronic device (100), foreground objects and background objects of the image. Further, the method includes determining, by the electronic device (100), a context of the foreground objects and a context of the background objects in the image. Further, the method includes determining, by the electronic device (100), a background pattern based on the context of the foreground objects and the context of the background objects. Further, the method includes modifying, by the electronic device (100), the background of the image based on the background pattern. Further, the method includes displaying, by the electronic device (100), the image with the modified background.

Abstract: An electronic device and a method for human segmentation in an image are provided. The method includes obtaining the image comprising at least one human, performing face detection to detect the human in the image, generating a first human segmentation from the image by comparing a pre-defined full human body template and the detected human, generating a second human segmentation from the image, by inputting the image to an artificial intelligence trained model and obtaining a composite human segmentation representing the detected human by using both of the first human segmentation and the second human segmentation.

Abstract: A method for processing image frames, and an electronic device are provided. The method includes receiving image frames captured by an image sensor at a first frame rate and storing the received image frames in a memory. A change is detected in the stored image frames and an image frame is interpolated between the stored image frames after detecting the change. The interpolated image is stored in the memory.

Abstract: A method and a system for recording a Super Slow Motion (SSM) video in a portable electronic device are provided. A SSM video recording system detects presence of motion in the image frames captured by the image capturing device. Upon detecting the presence of motion, the SSM video recording system generates a trigger, subsequent to which, a first and fourth set of image frames for performing a frame skipping mechanism, and a second and third set of image frames for performing an interpolation mechanism are selected. The image frames of desired frame rate generated by performing the frame skipping mechanism and the interpolation mechanism are transmitted to an encoder in an order, for encoding and recording. The method and system allow SSM even in mid-tier and low-tier models of smartphones, without the need of expensive specialized image sensors.

Abstract: A method and an apparatus for capturing a high quality dynamic image by setting a different row-wise exposure value when capturing a scene are provided. The dynamic image capturing method includes: generating an image by pre-capturing a scene via an event sensor; generating event data from the image; determining a row-wise exposure value of the image based on the event data; and determining a row-wise readout priority order of the image based on the row-wise exposure value of the image.

Abstract: A method and a system for recording a Super Slow Motion (SSM) video in a portable electronic device are provided. A SSM video recording system detects presence of motion in the image frames captured by the image capturing device. Upon detecting the presence of motion, the SSM video recording system generates a trigger, subsequent to which, a first and fourth set of image frames for performing a frame skipping mechanism, and a second and third set of image frames for performing an interpolation mechanism are selected. The image frames of desired frame rate generated by performing the frame skipping mechanism and the interpolation mechanism are transmitted to an encoder in an order, for encoding and recording. The method and system allow SSM even in mid-tier and low-tier models of smartphones, without the need of expensive specialized image sensors.