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: A method and apparatus for capturing images with a high degree of movements in a captured scene are provided. The method includes receiving, by an image sensor, a sequence of images including a plurality of RGB image frames and color event data, generating a plurality of pseudo color filter array (CFA) frames based on the color event data, and generating dynamic images by combining the plurality of RGB image frames and the plurality of pseudo CFA frames.

Abstract: A method and apparatus for capturing images with a high degree of movements in a captured scene are provided. The method includes receiving, by an image sensor, a sequence of images including a plurality of RGB image frames and color event data, generating a plurality of pseudo color filter array (CFA) frames based on the color event data, and generating dynamic images by combining the plurality of RGB image frames and the plurality of pseudo CFA frames.

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 apparatus for capturing images with a high degree of movements in a captured scene are provided. The method includes receiving, by an image sensor, a sequence of images including a plurality of RGB image frames and color event data, generating a plurality of pseudo color filter array (CFA) frames based on the color event data, and generating dynamic images by combining the plurality of RGB image frames and the plurality of pseudo CFA frames.