Abstract: A learning-based model is trained using a plurality of attributes of media. Depth estimation is performed using the learning-based model. The depth estimation supports performing a computer vision task on the media. Attributes used in the depth estimation include scene understanding, depth correctness, and processing of sharp edges and gaps. The media may be processed to perform media restoration or the media quality enhancement. A computer vision task may include semantic segmentation.

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 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 learning-based model is trained using a plurality of attributes of media. Depth estimation is performed using the learning-based model. The depth estimation supports performing a computer vision task on the media. Attributes used in the depth estimation include scene understanding, depth correctness, and processing of sharp edges and gaps. The media may be processed to perform media restoration or the media quality enhancement. A computer vision task may include semantic segmentation.

Abstract: A device and a method for capturing media by using a device including a plurality of flaps are provided. At least two flaps among the plurality of flaps each include at least one camera. The method includes analyzing preview images of the cameras based on a first media capture mode, adjusting a bend angle between the at least two flaps based on the analysis of the preview images to determine at least one baseline distance, and obtaining at least one media in the first capture mode at the at least one baseline distance.

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 device and a method for capturing media by using a device including a plurality of flaps are provided. At least two flaps among the plurality of flaps each include at least one camera. The method includes analyzing preview images of the cameras based on a first media capture mode, adjusting a bend angle between the at least two flaps based on the analysis of the preview images to determine at least one baseline distance, and obtaining at least one media in the first capture mode at the at least one baseline distance.

Abstract: An electronic device and method for capturing an image are disclosed. The electronic device includes an image sensor configured to capture images, a location sensor configured to detect a location of the electronic device, and a processor. The processor may execute the method, which includes capturing a first image, and detecting a first location where the first image is captured, detecting, by a processor, a second location at which a second image is to be captured and generating guidance information for travel to the second location, and when a present location is within a predefined range of the second location, automatically capturing the second image.

Abstract: A method and a device to compose an image by eliminating one or more moving objects in a scene being captured are provided. The method includes capturing plurality of images, generating a background image with a plurality of stationary objects after aligning the plurality of captured images, selecting a base image from a plurality of the aligned images, wherein the base image is selected based on a highest similarity measure with the background image, identifying the at least one moving object in the base image, and eliminating said identified at least one moving object in the base image to compose said image.

Abstract: An apparatus and a method for displaying images in an electronic device are provided. The electronic device includes a processor that obtains an image and a depth map corresponding to the image, separates the image into one or more areas based on the depth map of the image, applies an effect, which is different from at least one of other areas, to at least one of the areas separated from the image, and connects the areas, to which the different effects have been applied, as a single image, and a display that displays the single image.

Abstract: A method and a device to compose an image by eliminating one or more moving objects in a scene being captured are provided. The method includes capturing plurality of images, generating a background image with a plurality of stationary objects after aligning the plurality of captured images, selecting a base image from a plurality of the aligned images, wherein the base image is selected based on a highest similarity measure with the background image, identifying the at least one moving object in the base image, and eliminating said identified at least one moving object in the base image to compose said image.

Abstract: An electronic device and method for capturing an image are disclosed. The electronic device includes an image sensor configured to capture images, a location sensor configured to detect a location of the electronic device, and a processor. The processor may execute the method, which includes capturing a first image, and detecting a first location where the first image is captured, detecting, by a processor, a second location at which a second image is to be captured and generating guidance information for travel to the second location, and when a present location is within a predefined range of the second location, automatically capturing the second image.

Abstract: Disclosed is a method and apparatus for generating fixed size compressed images. The method includes grouping member entities of the image into a plurality of groups based on each image features, each of the plurality of groups including member entities sharing common features; selecting at least one group representative from at least one of the plurality of groups; estimating final control parameters for each of the group representatives in an iterative manner; and compressing the image based on the estimated control parameters.

Abstract: A method and an image capturing device configured to generate a defocused image from a reference image and one or more of focal bracketed images to provide an artificially defocused blurred image. The artificially defocused blurred image is a fusion image composed by processing the reference image and one or more of focal bracketed images to provide a clear foreground with gradual blurred background based on a created depth map. The method is time efficient as it provides faster processing on a captured and down sampled reference image and one or more captured down sampled aligned focal bracketed images. The depth map created using region based segmentation reduces a misclassification at a time of classifying foreground-background and misclassification of pixels to provide fast, robust artificial blurring of background in the captured reference image.