Abstract: The invention provides a method and an apparatus for adopting analogue decor templates in decor printing, including the similarity comparison between at least one of 1 to n hyperspectral digital reference images of the analogue decor template and at least one of 1 to n hyperspectral digital actual images of a substrate with decor. A decor-specific profile target is adjusted such that the digital template of the analogue decor template can be output on a substrate by way of an output medium in such a way that the colour deviation between at least one of 1 to n reference images of the analogue decor template and at least one of 1 to n actual images of the substrate with decor is below a specified target value.

Abstract: An image processing method includes converting an original image represented by a first color space into the original image represented by a second color space, where a correlation between color components of respective channels in the second color space of the original image is smaller than a correlation between color components of respective channels in the first color space; denoising channel images formed by the color components of the respective channels in the second color space respectively to obtain denoised images corresponding to the respective channel images; and obtaining a target image based on the denoised images.

Abstract: Embodiments relate to a front-end scaler circuit configured to receive and process demosaiced image data in different modes depending on if the demosaiced image data was demosaiced from Bayer or Quad Bayer raw image data. The front-end scaler circuit shares memory with a demosaicing circuit, and is configured to perform different operations that use different amounts of the shared memory based on the original image format of the demosaiced image data being processed, to compensate for additional memory utilized by the demosaicing circuit when demosaicing certain types of image data. For example, when processing image data demosaiced from Quad Bayer image data, the front-end scaler circuit discards a portion of the chrominance component data for the received image data before performing chromatic suppression, compared to when processing image data demosaiced from Bayer image data.

Abstract: A method includes obtaining a blended red-green-blue (RGB) image frame of a scene. The method also includes performing, using at least one processing device of an electronic device, an interband denoising operation to remove at least one of noise and one or more artifacts from the blended RGB image frame in order to produce a denoised RGB image frame. Performing the interband denoising operation includes performing filtering of red, green, and blue color channels of the blended RGB image frame to remove at least one of the noise and the one or more artifacts from the blended RGB image frame. The filtering of the red and blue color channels of the blended RGB image frame is based on image data of at least one of the green color channel and a white color channel of the blended RGB image frame.

Abstract: An electronic device according to various embodiments of the present invention may comprise: a camera module; a communication module for communicating with a second electronic device; and a processor for photographing a first screen displayed on a display of the second electronic device and an external background of the second electronic device by using the camera module, and acquiring a second screen for the second electronic device by using the first screen and the external background.

Abstract: Apparatuses, systems, and techniques for white balancing an image are presented. In at least one embodiment, a chromaticity-based weighting function is determined based at least on an estimated scene brightness of the image and applied to exclude or minimize the impact of large colored portions or objects within an image when estimating an illuminant color.

Abstract: A heads mounted display device is provided. The head-mounted display device includes a display, an optical system, and a processor. The display is configured to display a pre-warp image. The optical system is configured to receive the pre-warp image and output an undistorted image. The processor is configured to perform a tolerance enhancement operation on an original image to generate an enhanced image. Further, the processor is configured to apply a software distortion on the enhanced image to generate the pre-warp image. The software distortion is configured to compensate an optical distortion of the optical system.

Abstract: Methods, systems, devices, and tangible non-transitory computer readable media for saliency visualization are provided. The disclosed technology can include receiving a data input including a plurality of features. The data input can be segmented into regions. At least one of the regions can include two or more of the features. Attribution scores can be respectively generated for features of the data input. The attribution scores for each feature can be indicative of a respective saliency of such feature. A respective gain value for each region can be determined over one or more iterations based on the respective attribution scores associated with the features included in the region. Further, at each iteration one or more of the regions with the greatest gain values can be added to a saliency mask. Furthermore, at each iteration a saliency visualization can be produced based on the saliency mask.

Abstract: The present disclosure relates to an image processing device, an image processing method, and a program that can reduce the amount of processing required for a series of processing from detection to recognition of an object in a high-resolution image. An acquisition unit acquires, from a first resolution image, a second resolution image having a lower resolution than the first resolution image, a classification unit classifies an object included in the second resolution image, an identification unit identifies an object area corresponding to the object of a predetermined classification in the first resolution image, and a recognition unit performs recognition processing of the object on the object area identified in the first resolution image. The technology according to the present disclosure can be applied to a camera system of a remote control tower, for example.

Abstract: A media application generates training data that includes a first set of visual media items and a second set of visual media items, where the first set of visual media items correspond to the second set of visual items and include distracting objects that are manually segmented. The media application trains a segmentation machine-learning model based on the training data to receive a visual media item with one or more distracting objects and to output a segmentation mask for one or more segmented objects that correspond to the one or more distracting objects.

Abstract: A system for obtaining color imagery using SPADs includes a SPAD array that has a plurality of SPAD pixels. Each of the plurality of SPAD pixels includes a respective color filter positioned thereover. The system is configurable to capture an image frame using the SPAD array and generate a filtered image by performing a temporal filtering operation using the image frame and at least one preceding image frame. The at least one preceding image frame is captured by the SPAD array at a timepoint that temporally precedes a timepoint associated with the image frame. The system is also configurable to, after performing the temporal filtering operation, generate a color image by demosaicing the filtered image.

Abstract: An apparatus for acquiring images includes an image sensor and a signal processor. The image sensor may include a sensor substrate and a color separation lens array, wherein the sensor substrate includes a plurality of photo-sensing cells, and the color separation lens array may separate an incident light into a plurality of lights having different wavelengths and forms a phase distribution for condensing the plurality of lights having the different wavelengths on adjacent photo-sensing cells of the plurality of photo-sensing cells. The signal processor may perform deconvolution on sensing signals of the plurality of photo-sensing cells to obtain a sub-sampled image, perform demosaicing to restore a full resolution image having a full resolution from the sub-sampled image, and correct a color of the full resolution image using a point spread function (PSF) of the color separation lens array.

Abstract: A calculation method of food volume, a calculation method of food calories, an electronic apparatus, an electronic device and a computer-readable storage medium are provided. The calculation method of food volume includes: acquiring actual size information of a stereo reference object; acquiring a reference image corresponding to the stereo reference object and a food image corresponding to food to be estimated; and acquiring volume information of the food to be estimated, according to the actual size information of the stereo reference object, reference region size information acquired based on the reference image, and food region size information acquired based on the food image. The calculation method of food volume reduces the complexity of operation and enhances the flexibility of application.

Abstract: A system and method for real time discrete cosine transform image and video processing with convolutional neural network architecture. The system and method incorporate discrete cosine transform image processing with convolutional neural networks to achieve fast and efficient image processing that yields more reliable results than previously used image processing methods. The proposed system and method enable effective, real time, image processing which is applicable to a wide range of imaging and video devices.

Abstract: An apparatus comprises a receiver configured to receive, as a request for a remaining amount of a consumable item and a color for remaining amount display of the consumable item in the apparatus, information relating to a transmission source of the request, and a transmitter configured to transmit the remaining amount and the color to the transmission source in response to the request. The transmitter is configured to acquire a background color of the remaining amount display in response to the request, and when the background color acquired and a first display color set in advance as the color of the remaining amount display are determined to be close to each other according to a prescribed standard, transmit a second display color different from the first display color and the remaining amount to the transmission source.

Abstract: An auto white balance adjusting method includes determining a local white pixel area and a global white pixel area, selecting a plurality of pixels of an image according to the local white pixel area for generating a local average color value in a first color space, selecting a plurality of pixels of the image according to the global white pixel area for generating a global average color value in the first color space, converting the local average color value into three primary color gains in a second color space, generating three primary color target gains according to the three primary color gains and a color temperature curve in the second color space, and adjusting a white balance of the image frame by frame to meet the three primary color target gains according to the local average color value and the three primary color gains.

Abstract: A system is provided that includes a mobile user device that executes an application that receives a selection of a target skincare formulation from a user, and determines and transmits a recipe for generating the target skincare formulation that is based on a combination of a plurality of separate skincare formulation ingredients. The system includes a dispensing device configured to receive the transmitted recipe from the mobile user device and dispense each of the plurality of separate skincare formulation ingredients onto a common dispensing surface such that when the dispensed amounts of each of the plurality of separate skincare formulation ingredients is blended on the dispensing surface, the target skincare formulation is achieved. The mobile user device is configured to present at least one candidate priority skin condition to the user, that is addressed by the target skincare formulation, based on a user profile of the user of the smartphone.

Abstract: A method of multi-sensor white balance synchronization, and an electronic device using the same, including sensing a same scene by a plurality of sensors of an imaging system to obtain and provide outputs of the plurality of sensors; obtaining color information of the plurality of sensors and color statistical information of the plurality of sensors from the outputs of the plurality of sensors; merging the color information of the plurality of sensors based on the color statistical information of the plurality of sensors, to obtain global color information; calculating white balance gain coefficients of each of the plurality of sensors using the global color information; and generating an image having adjusted white balance based on the outputs of the plurality of sensors and the white balance gain coefficients.

Abstract: The image processing apparatus obtains a captured image including a foreground object and a background image not including the foreground object, adjusts pixel values of a pixel included in the background image, a color represented by the pixel values being in a specific color range, and generates an image representing an area of the foreground object based on a difference between the adjusted background image and the captured image.

Abstract: There is provided with an image processing apparatus that generates learning data for use in machine learning using a model. An obtaining unit is configured to obtain a first image for use in learning by the model. A generating unit is configured to, in a case where a first luminance of the first image is greater than a threshold, generate a second image with a second luminance different to the first luminance.

Abstract: A computing system generates a plurality of training datasets from past shoulder surgery cases and uses the plurality of training datasets to train a neural network. Each output layer neuron in a plurality of output layer neurons of the neural network corresponds to a different class in one or more shoulder pathology classification systems. The computing system may obtain a current input vector that corresponds to a current patient. Additionally, the computing system may apply the neural network to the current input vector to generate a current output vector. The computing system may determine, based on the current output vector, a recommended type of shoulder surgery for the current patient.

Abstract: An auto white balance adjusting method includes acquiring an image, allocating N windows inside the image according to color information of the image, filtering out M windows from the N windows for generating N-M windows according to feature information of the N windows, grouping the color temperatures of the N-M windows for generating at least one color temperature group according to a standard color temperature curve, setting a first weighting of the at least one color temperature group according to a correlation between the at least one color temperature group and the standard color temperature curve, setting a second weighting of the at least one color temperature group according to spatial information of the at least one color temperature group of the image, and adjusting a white balance of the image according to color information, the first weighting, and the second weighting.

Abstract: Techniques are generally described for highlight recovery for image sensors. In some examples, a first frame of image data comprising a first pixel may be received. The first pixel may include a value Ci,c where c?(R, G, B, IR). A white balance gained value Ci,cwb may be determined using Ci,c·WBc, where WBc represents a per-color channel white balance gain. A color-corrected value Ciccm may be determined based at least in part on Ci,cwb and a color correction matrix. An interpolation coefficient ? may be determined based at least in part on a clipped representation of the color-corrected value Ciccm. A second value Ciout may be determined for the first pixel by interpolating between an unclipped representation of the first pixel and an unclipped representation of Ciccm using ?.

Abstract: Techniques are disclosed for training and applying a denoising model. The denoising model includes multiple specialized denoisers and a generalizer, each of which is a machine learning model. The specialized denoisers are trained to denoise images associated with specific ranges of noise parameters. The generalizer is trained to generate per-pixel denoising kernels for denoising images associated with arbitrary noise parameters using outputs of the specialized denoisers. Subsequent to training, a noisy image, such as a live-action image or a rendered image, can be denoised by inputting the noisy image into the specialized denoisers to obtain intermediate denoised images that are then input, along with the noisy image, into the generalizer to obtain per-pixel denoising kernels, which can be normalized and applied to denoise the noisy image.

Abstract: The present disclosure describes a method and apparatus that uses spectral reconstruction of detector sensitivity to solve the above and other problems. Specifically, light sources (e.g., of visible light, infrared light and/or ultraviolet light) may target the sensor and emit, in sequence, beams of different wavelength. Spectral output of multiple channels of the sensor is received, the output including intensity (e.g., peak intensity) for each channel. The output is compared with reference intensities for each of the light sources and difference values are calculated. Based on the difference values, a calibration matrix is calculated that can be used in calibrating the specific sensor. In addition, the present disclosure describes an apparatus that includes a plurality of light sources, a receiver for receiving sensor output, and circuitry configured to generate a calibration matrix using the light sources, the receiver using spectral reconstruction of detector sensitivity.

Abstract: An information processing apparatus, an information processing system, an information processing method, and a non-transitory recording medium. The information processing apparatus acquires a print job, selects printing paper according to an operation on an operation device, acquires a color conversion dictionary from among color conversion dictionaries corresponding to respective representative paper sorted into a plurality of groups based on physical property of the selected printing paper, presents information on the color conversion dictionary through a display, receives an operation input indicating whether to accept or to reject a presented color conversion dictionary through the operation device, and converts color information of the print job using the color conversion dictionary accepted by the operation input received through the operation device.

Abstract: Systems and methods for processing image data representing a document to remove deformations contained in the document are disclosed. A system may include one or more memory devices storing instructions and one or more processors configured to execute the instructions. The instructions may instruct the system to provide, to a machine learning system, a training dataset representing a plurality of documents containing a plurality of training deformations. The instructions may also instruct the system to use the machine learning system to process image data representing a target document containing a target document deformation. The machine learning system may generate restored image data representing the target document with the target document deformation removed. The instructions may further instruct the system to provide the restored image data to at least one of a graphical user interface, an image storage device, or a computer vision system.

Abstract: An estimation device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: generate a plurality of extraction regions by adding a perturbation to an extraction region of a partial image determined based on positions of feature points extracted from a face image; estimate a plurality of directions of at least one of a face and a line of sight and a reliability of each of the plurality of directions based on a plurality of partial images in the plurality of extraction regions of the face image; and calculate an integrated direction obtained by integrating the plurality of directions based on the estimated reliability.

Abstract: A method of operation of a compute system includes: verifying a patient image as an acceptable image; detecting a body part from the acceptable image; generating a skin segmentation for the body part including a non-skin region and a skin region based on the acceptable image; and generating a local Hidradenitis Suppurativa (HS) severity score based on the patient image for displaying on a device to assist in diagnosis.

Abstract: Techniques are generally described for color filter array interpolation of image data. A first frame of image data representing a plurality of pixels arranged in a grid may be received. A low pass filter may be used to generate a first luminance value for a first non-green pixel of the plurality of pixels. A first chrominance value of the first non-green pixel may be determined based at least in part on a combination of at least a chrominance value of a first green pixel located adjacent to the first non-green pixel and a chrominance value of a second green pixel located adjacent to the first non-green pixel. A second luminance value for the first non-green pixel may be determined based on a combination of the first chrominance value and the first luminance value. Output values for the first non-green pixel may be determined based at least in part on the second luminance value.

Abstract: A method to obtain and process data to generate intrinsic hyper-spectral data cubes is provided, where a data acquisition procedure involves scanning the field of view under focused and diffused conditions and an intrinsic calibration procedure requires focused and diffused scans of the field of view of a white reference surface. The spectra of the diffuse scan of the white reference surface is subtracted from the spectra of the focused scan of the white reference surface resulting in a residual data cube of a scan of the white surface. To obtain an intrinsic data cube of a field of view of interest, the residual data cube is added to the diffused data cube of the field of view of interest generating a resulting data cube that is subtracted from the focused data cube field of view of interest.

Abstract: Methods and decompression units for decompressing a selected sub-block of image element values from a compressed block of image element values. The method includes: identifying, from the compressed block of image element values, a pattern of a plurality of patterns of image element values associated with the selected sub-block; identifying, from the compressed block of image element values, one or more image element values associated with the selected sub-block; and generating the selected sub-block from the pattern and the one or more image element values associated with the selected sub-block.

Abstract: A display device for displaying an image through a lens specifies a position of a pupil of a user on the basis of a gazing point on a display panel. Parameters used to correct lens distortions of images in red, green, and blue included in data of a display image are prepared in association with representative points of gazing points. The display device interpolates parameters on the basis of a positional relation between the position of an actual gazing point or a predicted value thereof and a representative point, and corrects the display image per primary color using the interpolated parameters, thereby correcting chromatic aberrations.

Abstract: An image processing device, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: obtain kernel coefficient information corresponding to each pixel of a plurality of pixels included in a first image, using a convolution neural network including one or more convolution layers, generate a spatially variant kernel including a kernel corresponding to the each pixel, based on a gradient kernel set including a plurality of gradient kernels corresponding to one or more gradient characteristics of the plurality of pixels, and the kernel coefficient information, and generate a second image, by applying the kernel included in the spatially variant kernel to a region centered on the each pixel, and filtering the first image.

Abstract: The present disclosure is related to systems and methods for image processing. The method includes obtaining an image including a plurality of pixels. The method includes determining at least one brightness parameter based on the brightness values of the plurality of pixels. The method includes obtaining an image processing algorithm including at least one image processing parameter. The method includes determining a value of the at least one image processing parameter based on the at least one brightness parameter. The method includes generating a target image by processing the image according to the image processing algorithm upon the value of the at least one image processing parameter.

Abstract: A visualization system including a video processing apparatus (VPA) including a processor; and memory having processing instructions stored therein and executable by the processor, the processing instructions operable to, when executed by the processor: determine an amplification gain level applied by the image sensor; determine, based on the amplification gain level, a denoising level and a corresponding sharpening level; and process image data to denoise and sharpen an image corresponding to the image signals using the denosing level and the sharpening level.

Abstract: According to an embodiment of the present disclosure, an electronic device comprises: a camera; a display; a memory; and at least one processor electrically connected to the display and the memory. The processor obtains an input of selecting a first image, generates a color filter by comparing the first image and a second image having a color pattern different from the first image, obtains a third image, and generates a fourth image by applying the generate color filter to the third image. The processor derives a function by comparing color values of pixels of the first image and the second image, and coefficients of the function. The color filter may include the function derived by comparing the first image and the second image, the coefficients of the function, and a color lookup table generated on the basis of the derived function. The processor stores the generated color filter in the memory and generates the fourth image by applying the stored color filter to the third image.

Abstract: A system and method for adaptive color assignments to image labels during annotation of datasets includes preparing a dataset for image labeling by an annotator by: leveraging a global color analyzer to perform a global color distribution of a plurality of images to identify one or more overall colors present in the plurality of images, and a local color analyzer to perform a local color distribution for each image to identify one or more colors present in an area of interest of the image, and selecting a plurality of candidate colors to be used as image labels by the annotator, based on an output of the global color analyzer and an output of the local color analyzer.

Abstract: An image processing method includes: performing content analysis upon an input frame to generate at least one content analysis result of the input frame; determining, by a processing circuit, an anti-blue light strength level for the input frame according to content analysis result(s) of the input frame; and in response to the anti-blue light strength level determined for the input frame, performing color correction upon the input frame to generate an output frame.

Abstract: An endoscope apparatus includes an illumination device emitting a plurality of lights with different wavelength bands at least two different timings in time series, and a processor including hardware. The plurality of lights includes a first light and a second light. The processor is configured to generate a highlighted image in which a specific structure under a mucus membrane is highlighted, based on a plurality of images obtained by the emission of the plurality of lights. The illumination device emits the first light and the second light continuously. The first light has a first wavelength band corresponding to a luminance component of the highlighted image. The second light has a second wavelength band that enables capturing of an image of the specific structure with higher contrast than the first light.

Abstract: The present disclosure relates to systems and methods for image processing. The system may obtain low-frequency component of a first image. For each element of the first image, the system may adjust a luminance of the element in response to determining that the luminance of the element is less than a predetermined luminance threshold. The system may determine a first luminance weight map corresponding to the first image based on the adjusted luminance of each element of the first image. The system may obtain low-frequency component of a second image and determine a second luminance weight map corresponding to the second image based on a luminance of each element of the second image. The system may further determine a fused image based on the low-frequency component of the first image, the first luminance weight map, the low-frequency component of the second image, and the second luminance weight map.

Abstract: A method of automated control of a microscope in cryogenic electron microscopy (cryo-EM), wherein the microscope is configured to collect high-magnification micrographs of particles suspended in vitreous ice. Such particles are found in grid squares, and a square contains holes from which high-magnification micrographs are imaged. The method is carried out during an active data collection session, leveraging a pipeline that comprises a set of models. The pipeline evaluates a set of collection locations to determine whether to continue collection at a current grid/square or instead at a new grid/square. The evaluation is based on a set of one or more quality scores derived from one or more pretrained models and machine learning-based active learning. Based on the determination, control information is provided to automatically control the microscope to move to a next target for data collection.

Abstract: A high-dynamic-range (HDR) camera module with adaptive image data linearization includes (i) an HDR image sensor configured to generate tone-compressed HDR images as respective frames that include active pixel data and metadata, (ii) a processor outside the HDR image sensor, and (iii) a memory outside the HDR image sensor and storing machine-readable instructions that, when executed by the processor, control the processor to: (a) extract, from a frame of a first tone-compressed HDR image, tone-compressed pixel intensities from the active pixel data and a histogram of pre-tone-compression pixel intensities from the metadata, (b) derive, from the tone-compressed pixel intensities and the histogram, a correspondence between tone-compressed pixel intensities and pre-tone-compression pixel intensities, and (c) linearize at least a portion of the active pixel data of either the first tone-compressed HDR image or a subsequent tone-compressed HDR image, according to the correspondence, to produce a linearized HDR ima

Abstract: An image processing system according to the present disclosure includes: an image acquisition unit configured to acquire a monochrome image of a subject including a target part; a hint acquisition unit configured to acquire a category of the target part and a first color hint indicating a color of the target part; a hint conversion unit configured to convert the first color hint into a second color hint indicating the color of the target part based on the acquired category; and a colorization generation unit configured to generate a colorized image corresponding to the monochrome image from the monochrome image and the second color hint of the target part by using a prediction model trained by machine learning.

Abstract: A colorimeter includes: a colorimetric section that performs colorimetry; a first wireless communicating section that performs first wireless communication according to a first wireless communication standard; a second wireless communicating section that performs second wireless communication according to a second wireless communication standard; and a processor that controls the first wireless communicating section and the second wireless communicating section. The processor transmits colorimetric data acquired by the colorimetry in the first colorimetric mode by the first wireless communication when colorimetry in a first colorimetric mode is performed. The processor also transmits colorimetric data acquired by the colorimetry in the second colorimetric mode by the second wireless communication when colorimetry in a second colorimetric mode is performed.

Abstract: A method for encoding a LUT defined as a lattice of vertices is disclosed. At least one value is of each vertex of the lattice. The method comprises for a current vertex: predicting the at least one value of said current vertex from another value which is for example obtained from reconstructed values of neighboring vertices; and encoding in a bitstream at least one residue computed between the at least one value of the current vertex and its prediction in a bitstream.

Abstract: A method implemented by a video coding apparatus. The method includes applying an output of a neural network (NN) filter to an unfiltered sample of a video unit to generate a residual, applying a scaling function to the residual to generate a scaled residual, adding another unfiltered sample to the scaled residual to generate a filtered sample, and converting between a video media file and a bitstream based on the filtered sample that was generated. A corresponding video coding apparatus and non-transitory computer readable medium are also disclosed.

Abstract: A method for increasing a dynamic range of a dual-pixel image sensor is described. The method includes detecting an intensity level of a full pixel from a plurality of pixels of an optical sensor, one or more full pixels of the plurality of pixels includes at least two sub-pixels, detecting an intensity level of one or more sub-pixels, detecting that the intensity level of the full pixel of the optical sensor has reached a saturation level of the full pixel, and in response to detecting that the intensity level of the full pixel of the optical sensor has reached the saturation level of the full pixel, computing an extrapolated intensity level of the full pixel based on the intensity level of the one or more sub-pixels.

Abstract: Provided is a solid-state imaging apparatus and an electronic device, in which a scale of circuits, to perform arithmetic operation of the neural network, is suppressed. A solid-state imaging apparatus according to an aspect of the present disclosure includes a pixel array unit and a processing unit. The pixel array unit has a plurality of first pixels that generate electric signals, which have a logarithmic characteristic with respect to light quantity, as first pixel signals. The processing unit performs arithmetic processing of the first neural network based on a plurality of first input data, which are based on the plurality of first pixel signals read from the pixel array unit, and a plurality of logarithmic weighting factors which express strength of the connection between the plurality of first nodes by a logarithm.

Abstract: The invention discloses a method to combine low-dynamic range images with different exposures to construct a high-dynamic range image. The method deals with the motions such that to minimize the chance of such “non-recoverable” occlusions and avoids the necessity of knowing or finding the response functions of the low-dynamic range images. Apart from that, the method is fast enough to be used in a mobile camera device and produce good user experience.