Abstract:
Disclosed is a color transform method performed by an electronic device that includes generating an initial color transformed image by performing color transform on a raw image, determine noise amplification degrees indicating degrees to which noise included in the raw image is amplified by the color transform, processing the determined noise amplification degrees, and generating a final color transformed image by filtering the generated initial color transformed image using the processed noise amplification degrees and at least one of the raw image or luminance information of the raw image.
Abstract:
A method and apparatus for color space conversion are provided. An apparatus for converting a color space includes at least one processor configured to convert an original full image of an original color space to a temporary image of a target color space, estimate a color space mapping parameter between the original color space and the target color space, the color space mapping parameter corresponding to the original full image, obtain a residual vector of the target color space based on the temporary image and the color space mapping parameter, convert the residual vector to a residual image of the target color space, and obtain a target full image of the target color space by combining the residual image with the temporary image.
Abstract:
An image processing method includes adjusting a brightness of each of a first image and a second image based on a first exposure time when the first image is captured and a second exposure time when the second image is captured, respectively, the first and second images being generated by capturing a same object under different light conditions, estimating an intensity of light, reaching the object when the second image is captured, based on the adjusted brightness of each of the first and second images, separating the second image into two or more regions according to the estimated intensity of light, determining a target brightness that a final result image is to have, adjusting the brightness of the second image, by different amounts for each of the separated regions, based on the target brightness and generate the final result image based on the adjusted brightness of the second image.
Abstract:
A method and an electronic device with image registration are provided. The method includes generating a first optical flow between a first partial image and a second partial image, which are captured by respective first and second cameras using an optical flow estimation model; generating disparity information between the first partial image and a third partial image, captured by a third camera, based on depth information of the first partial image generated using the first optical flow; and estimating a second optical flow between the first partial image and the third partial image based on the generated disparity information for generating a registration image.
Abstract:
A method of learning a parameter of a sensor filter and an apparatus for performing the method are provided. The learning method may include performing a simulation on a target image for each spectrum of a sensor filter, obtaining an output value by inputting the simulated image to a vision model for a vision task, and learning a parameter of the sensor filter based on a loss between a label of the vision model and the output value of the vision model.
Abstract:
A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.
Abstract:
A processor-implemented imaging method includes: obtaining initial homography information between a plurality of tele images that covers a field of view (FOV) of a wide image; receiving a user input of zooming a partial region of the wide image from a screen on which the wide image is displayed; stitching tele images corresponding to the partial region using the initial homography information, based on whether a zoom level corresponding to the user input exceeds a maximum zoom level of the wide image; and rendering the stitched tele images and displaying an image obtained by the rendering on the screen.
Abstract:
Disclosed are target tracking methods and apparatuses. The target tracking apparatus performs target tracking on an input image obtained in a first time period within a single time frame, using a light neural network in a second time period of the single time frame. The target tracking apparatus may perform target tracking on input images generated within the same time frame.