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: The present disclosure relates to method and apparatus for removing shadows from a High Resolution (HR) image. The HR image including shadows is received. The HR image converted into Low Resolution (LR) image. LR shadow-free image is generated from the LR image using first Artificial Intelligence (AI) model. A HR shadow-free image is generated based on combination of the LR shadow-free image and the HR image using second AI model. The present disclosure provides an efficient framework and lightweight network architectures which are used to remove the shadows based on corresponding shadow characteristics. The framework can be deployed in resource-constrained platforms. The present disclosure facilitates processing selected areas of the HR image and retaining the unselected area. Thus, less computational resources are required.

Abstract: A method for real-time semantic image segmentation using a monocular event-based sensor includes capturing a scene using a red, green, blue (RGB) sensor to obtain a plurality of RGB frames and an event sensor to obtain event data corresponding to each of the plurality of RGB frames, performing object labeling for objects in a first RGB frame among the plurality of RGB frames by identifying one or more object classes, obtaining an event velocity of the scene by fusing the event data corresponding to the first RGB frame and at least one subsequent RGB frame among the plurality of RGB frames, determining whether the event velocity is greater than a predefined threshold, and performing object labeling for objects in the at least one subsequent RGB frame based on the determination.

Abstract: A method for real-time semantic image segmentation using a monocular event-based sensor includes capturing a scene using a red, green, blue (RGB) sensor to obtain a plurality of RGB frames and an event sensor to obtain event data corresponding to each of the plurality of RGB frames, performing object labeling for objects in a first RGB frame among the plurality of RGB frames by identifying one or more object classes, obtaining an event velocity of the scene by fusing the event data corresponding to the first RGB frame and at least one subsequent RGB frame among the plurality of RGB frames, determining whether the event velocity is greater than a predefined threshold, and performing object labeling for objects in the at least one subsequent RGB frame based on the determination.

Abstract: An image processing method, including preparing a first image; designating at least one region of interest (ROI) in the first image, generating restoration data; and generating a second image using the restoration data, the second image corresponding to the ROI, wherein a resolution of the second image is higher than a resolution of a region corresponding to the ROI in the first image.

Abstract: Embodiments utilize a camera on a universal remote control to automatically detect which device the remote is pointed at by the user. The image captured by the camera is compared to a database of images of known or preconfigured devices that can be controlled by the universal remote control. If the image captured by the camera matches an image in the database, then the universal remote control loads a specific key map for the preconfigured device from the database.

Abstract: An image processing method, including preparing a first image; designating at least one region of interest (ROI) in the first image, generating restoration data; and generating a second image using the restoration data, the second image corresponding to the ROI, wherein a resolution of the second image is higher than a resolution of a region corresponding to the ROI in the first image.

Abstract: Embodiments utilize a camera on a universal remote control to automatically detect which device the remote is pointed at by the user. The image captured by the camera is compared to a database of images of known or preconfigured devices that can be controlled by the universal remote control. If the image captured by the camera matches an image in the database, then the universal remote control loads a specific key map for the preconfigured device from the database.

Abstract: Method and system for auto-focusing. The method includes checking for presence of a subject in a first frame. The method also includes determining a second frame indicative of an in-focus position of the subject if the presence of the subject is detected. The method further includes causing to focus on the subject based on the second frame. Further, the method includes checking for a change in at least one of location of the subject in the second frame, and sharpness and luminance in an area corresponding to the subject in the second frame. Moreover, the method includes causing to refocus on the subject if the change is detected.

Abstract: Method and system for auto-focusing. The method includes checking for presence of a subject in a first frame. The method also includes determining a second frame indicative of an in-focus position of the subject if the presence of the subject is detected. The method further includes causing to focus on the subject based on the second frame. Further, the method includes checking for a change in at least one of location of the subject in the second frame, and sharpness and luminance in an area corresponding to the subject in the second frame. Moreover, the method includes causing to refocus on the subject if the change is detected.