Abstract: An image sensor includes a color filter array including a first color filter including a first number of blue pass filtering elements, the first number of red pass filtering elements, and green pass filtering elements in a first pattern, wherein a number of the green pass filtering elements in the first pattern is twice the first number, and a second color filter including a second number of blue pass filtering elements, the second number of red pass filtering elements, and green pass filtering elements in a second pattern, wherein a number of green pass filtering elements in the second pattern is twice the second number, and the second number is greater than the first number. The second color filter may surround an area of the first color filter.

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 display device includes a substrate, a light emitting element layer disposed on the substrate and including a plurality of light emitting areas each including a light emitting element that emits light, an encapsulation layer disposed on the light emitting element layer, and a light control layer disposed on the encapsulation layer, wherein the light control layer includes a light transmitting film that transmits the light and a light blocking film that blocks the light, and the light transmitting film includes, a first light transmitting portion overlapping the light blocking film in a thickness direction of the substrate, and a plurality of second light transmitting portions each surrounded by the light blocking film, and a thickness of the light blocking film is greater than a thickness of the first light transmitting portion.

Abstract: An imaging device includes an imaging lens array including a plurality of imaging lenses each with a same focal length, the plurality of imaging lenses being configured to capture a scene; a sensing array comprising a plurality of sensors configured to receive light passing through the plurality of imaging lenses; a processor configured to determine a scene depth plane corresponding to the scene based on a comparison, for each of a plurality of candidate depths, between reference pixel values based on first sensors of the plurality of sensors corresponding to a reference imaging lens of the plurality of imaging lenses and target pixel values that are based on second sensors of the plurality of sensors corresponding to a target imaging lens of the plurality of image lenses; and a lens driver configured to position the imaging lens array to be disposed at a point corresponding to the scene depth plane.

Abstract: A method and apparatus for array image processing are provided. The method includes receiving sub images corresponding to different views of an input array image generated through an array lens, generating temporary restored images based on the sub images using a gradient between neighboring pixels of each of the sub images, determining matching information based on a view difference between pixels of the sub images using a neural network model, based on a pixel distance between matching pairs of the pixels of the sub images using the matching information, extracting refinement targets from the matching pairs, refining the matching information by replacing at least some of target pixels included in the refinement targets based on a local search of a region based on pixel locations of the refinement targets, and generating an output image of a single view by merging the temporary restored images based on the refined matching information.

Abstract: An optimization method includes determining first information related to a difference between the color filter spectrum and the color spectrum or a difference between an image transformed by the color transformation matrix and a ground truth (GT) image in the reset color space, determining second information representing smoothness of the color filter spectrum, third information representing a transmittance of the color filter spectrum, calculating a cost value based on the first information, the second information, and the third information, compare the calculated cost value and a threshold, and updating one of the filter spectrum information and the color transformation matrix in response to the calculated cost value being equal to or greater than the threshold.

Abstract: An image restoration method and apparatus are provided. The image restoration method includes acquiring a target image, and acquiring a restoration image of the target image from an image restoration model to which the target image and pixel position information of the target image are input.

Abstract: An apparatus having a lens assembly array and a method of using the apparatus are provided. The apparatus includes a lens assembly array including a plurality of lens assemblies each including a plurality of individual lenses in a form of an array, an actuator assembly configured to move at least a portion of the plurality of lens assemblies in at least one direction, and a single image sensor configured to generate image data including a plurality of sub-images corresponding to the plurality of lens assemblies based on light passing through the lens assembly array.

Abstract: An image sensor includes a plurality of lens elements, each lens element of the plurality of lens elements including a plurality of scatterers arranged to concentrate light incident on the image sensor; and a sensing element configured to sense light passing through the plurality of lens elements, wherein one lens element of the plurality of lens elements has a first focal length that is different from a second focal length of another lens element of the plurality of lens elements and is separated from the sensing element by the first focal length.

Abstract: An image sensor includes a plurality of lens elements, each lens element of the plurality of lens elements including a plurality of scatterers arranged to concentrate light incident on the image sensor; and a sensing element configured to sense light passing through the plurality of lens elements, wherein one lens element of the plurality of lens elements has a first focal length that is different from a second focal length of another lens element of the plurality of lens elements and is separated from the sensing element by the first focal length.

Abstract: A method of driving a lens array camera may include simultaneously driving a first group of sensing elements from among a plurality of sensing elements, each sensing element from among the first group of sensing elements corresponding to a same original signal viewpoint, wherein the plurality of sensing elements is included in a sensor corresponding to the lens array camera including N rows of N lenses, N being a natural number.

Abstract: An image sensor includes a plurality of thin lens elements, each of the plurality of thin lens elements including a plurality of scatterers configured to concentrate light of a partial wavelength band among light incident on the image sensor. The image sensor further includes a micro lens array configured to concentrate light of another wavelength band wider than the partial wavelength band, and a sensing element configured to sense light passing through the plurality of thin lens elements and the micro lens array.

Abstract: A method for processing an image by using a neural network is provided. The method includes obtaining a target image; and obtaining a restored image corresponding to the target image by using the neural network, the neural network including a plurality of residual blocks connected sequentially. The obtaining the restored image includes obtaining the restored image by performing, in each residual block of the plurality of residual blocks, image processing on an input image, which is input from a previous residual block, on different scales, and providing results of the image processing performed on the different scales to a subsequent residual block.

Abstract: Provided are an image processing method and an image processing device. The image processing method includes generating an image based on viewpoint information of a user; rendering the image based on information about what is in front of the user; and outputting the rendered image using an optical element.

Abstract: An imaging device is provided. The imaging device may sense light passing through a corresponding imaging lens and a corresponding color filter in sensing elements disposed in a sensing region for each color channel, and generate sensing data based on a grouping of color intensity values sensed by the sensing elements for each sensing region based on a binning size determined based on an illuminance of light.

Abstract: An apparatus with image processing includes: one or more processors configured to: generate a full sampling image by demosaicing a first color filter array (CFA)-based input image; based on a trained color conversion model trained for color conversion and noise suppression of the full sampling image, determine a bias and a conversion matrix corresponding to each pixel of the full sampling image; and based on the conversion matrix and the bias, convert a color space of the full sample image, corresponding to the first CFA, into a color space corresponding to a second CFA.

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 restoration apparatus generates a plurality of warped images by warping each of a plurality of input images based on respective depths corresponding to a plurality of disparities, and generates an output image with a high resolution based on the plurality of input images and the plurality of warped images using an image restoration model.

Abstract: Provided is an image restoration apparatus and method. The image restoration apparatus may store an image restoration model including a convolutional layer corresponding to kernels having various dilation gaps, and may restore an output image from a target image obtained by rearranging a compound eye vision (CEV) image by the image restoration model.

Abstract: An electronic device includes an image sensor including a color filter having a plurality of color channels, a memory storing a plurality of color conversion matrices and instructions, and a processor. The processor is configured to obtain noise information of a color image captured by the image sensor, select a target matrix from among the plurality of the color conversion matrices based on the obtained noise information, and generate a color converted image by applying the selected target matrix to the color image.