Abstract: Provided are an image processing apparatus and an operation method of the image processing apparatus. The image processing apparatus includes a memory storing one or more instructions, and a processor configured to execute the one or more instructions stored in the memory to, by using one or more convolution neural networks, extract target features by performing a convolution operation between features of target regions having same locations in a plurality of input images and a first kernel set, extract peripheral features by performing a convolution operation of features of peripheral regions located around the target regions in the plurality of input images and a second kernel set, and determine a feature of a region corresponding to the target regions in an output image, based on the target features and the peripheral features.

Abstract: A method for edge identification of a self-shadowing object based on a visual camera and a vehicle includes: collecting image data of an object to be identified, and performing differentiation processing; according to a preset threshold, performing three-value processing to the image that has been differentiation-processed, to acquire a three-value-processed image including positive-direction boundary pixels and negative-direction boundary pixels; according to the positive-direction boundary pixels and the negative-direction boundary pixels, acquiring a positive-direction straight linear segment and a negative-direction straight linear segment that represent boundary trends of the object to be identified; if the straight linear segments do not create a target object that satisfy a predetermined condition, determining the first boundary according to the predetermined condition; according to the first boundary, determining the second boundary; and by using positions of the first boundary and the second boundary

Abstract: A display system for a welding mask that includes a darkening filter layer, a light field camera system to capture a light field, and an optical image stabilization subsystem. Captured light fields are displayed via a light field display within the welding helmet without risk of overexposure of ultraviolet radiation to the operator. In some examples, dual light field displays are used to display different images to each eye of the operator.

Abstract: A three-dimensional holographic display device includes a light emitting diode (LED) array including a plurality of light sources controlled to sequentially output light according to a preset pattern, a lens configured to refract light incident from the LED array, a spatial light modulator (SLM) configured to modulate light incident from the lens, and a processor configured to generate a plurality of holographic signals each comprising depth information adjusted according to an arrangement location of each of the plurality of light sources, and for each of the plurality of light sources, control the SLM to modulate the light based on a holographic signal corresponding to the light source.

Abstract: The present disclosure relates to systems and methods for image reconstruction. The system may obtain an initial image to be processed. The system may generate a reconstructed image by performing a plurality of iteration steps on the initial image. Each of the plurality of iteration steps may include a first optimization operation and at least one second optimization operation. The first optimization operation and the at least one second optimization operation may be executed sequentially. The first optimization operation may include receiving an image to be processed in the iteration step and determining an updated image by preliminarily optimizing the image to be processed. The at least one second optimization operation may include determining an optimized image by reducing interference information of the updated image and designating the optimized image as a next image to be processed in a next iteration step. The interference information may include noise information and/or artifact information.

Abstract: An apparatus includes a control unit configured to control a position of a focus lens of the apparatus, a setting unit configured to change a threshold for detecting a luminance change in each pixel in a predetermined range, an acquisition unit configured to acquire a signal indicating a pixel in which a luminance change that is more than or equal to the threshold has occurred, and a determination unit configured to determine an in-focus position of the focus lens based on the signal detected depending on the position of the focus lens and the set threshold.

Abstract: A method for concealing data in an image or a video stream inside a compression chain, being implemented by a computer and including a structuring and processing phase, during which at least one image is structured into blocks including coefficients, a phase of converting and quantifying blocks so as to generate converted and quantified coefficients, with entropic coding intended to code the converted and quantified coefficients. The method includes a step of concealing data, during which bits of the data are concealed by modifying converted and quantified coefficients located in a high frequency zone of at least some of the blocks that relate to the luminance component of the video stream and that are intra-coded. This modification is performed after the quantification but before the entropic coding.

Abstract: The present disclosure provides a method and device for correcting a scanned image, and an image scanning system, and relates to the field of image scanning. The method includes obtaining a scanned image of a scanned object, detecting one or more reference objects from the scanned image, determining a deformation parameter of each reference object of the one or more reference objects based on preset a standard parameter of the each reference object, and correcting the scanned image based on the deformation parameters of the one or more reference objects.

Abstract: An AR calibration system for correcting AR headset distortions. A calibration image is provided to an external screen and viewable through a headset reflector, and an inverse of the calibration image is provided to a headset display, reflected off the reflector and observed by a camera of the system while it is simultaneously observing the calibration image on the external screen. One or more cameras are located to represent a user's point of view and aligned to observe the inverse calibration image projected onto the reflector. A distortion mapping transform is created using an algorithm to search through projection positions of the inverse calibration image until the inverse image observed by the camera(s) cancels out an acceptable portion of the calibration image provided to the external screen as observed through the reflector by the camera, and the transform is used by the headset, to compensate for distortions.

Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.

Abstract: A method of video decoding performed in a video decoder includes receiving compressed geometric data corresponding to a three dimensional (3D) space. The method further includes generating a resolution occupancy map. The method further includes reconstructing a point cloud using the compressed geometric data and the resolution occupancy map. The method further includes performing a smoothing operation on one of the (i) reconstructed point cloud and (ii) the generated resolution occupancy map.

Abstract: In various embodiments, a prediction application computes a quality score for re-constructed visual content that is derived from visual content. The prediction application generates a frame difference matrix based on two frames included in the re-constructed video content. The prediction application then generates a first entropy matrix based on the frame difference matrix and a first scale. Subsequently, the prediction application computes a first value for a first temporal feature based on the first entropy matrix and a second entropy matrix associated with both the visual content and the first scale. The prediction application computes a quality score for the re-constructed video content based on the first value, a second value for a second temporal feature associated with a second scale, and a machine learning model that is trained using subjective quality scores. The quality score indicates a level of visual quality associated with streamed video content.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving mission specific data. Converting the mission specific data into attribute classes recognizable by image recognition classifiers, where the image recognition classifiers are pre-trained to detect objects corresponding to the attributed classes within digital images. Obtaining, from a wearable camera system, low resolution images of a scene and high resolution images of the scene. Detecting, within the low resolution images using a low resolution image classifier, an object that corresponds to one of the attribute classes. In response to detecting the object that corresponds to one of the attribute classes, providing, for presentation to a user of the wearable camera system by a presentation system, an first alert indicating a potential detection of the suspect, and providing the high resolution images to a high resolution image classifier.

Abstract: A method for determining whether or not a transparent protective cover of a video camera comprising a lens-based optical imaging system is partly covered by a foreign object is disclosed. The method comprises: obtaining (402) a first captured image frame captured by the video camera with a first depth of field; obtaining (404) a second captured image frame captured by the video camera with a second depth of field which differs from the first depth of field; and determining (406) whether or not the protective cover is partly covered by the foreign object by analysing whether or not the first and second captured image frames are affected by presence of the foreign object on the protective cover such that the difference between the first depth of field and the second depth of field results in a difference in a luminance pattern of corresponding pixels of a first image frame and a second image frame.

Abstract: The present disclosure discloses an image haze removal method and apparatus, and a device. The method includes: acquiring a hazy image to be processed; and obtaining a haze-free image corresponding to the hazy image by inputting the hazy image into a pre-trained haze removal model. The present disclosure uses the residual dual attention fusion modules as basic modules of the neural network, so that each feature map can obtain pixel features while enhancing the global dependence, thus improving the image dehazing effect.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and a set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image.

Abstract: A system, and method of operating the same detects moving targets in images and performs image turbulence correction. The system includes an automatic target recognizer (ATR) system including a database. The ATR includes a feature extractor and processor arranged to detect a plurality of reference features associated with targets within image frames, and calculate a position of the plurality of reference features. The system includes an image processor arranged to receive the position, demosaic the image frames into a plurality of video tiles, iteratively process the video tiles for turbulence correction to generate turbulence corrected video tiles associated with acquired targets; convert the turbulence corrected video tiles into a single video frame tile including turbulence degradation correction; and mosaic each of the single video frame tiles to generate a full field of view turbulence corrected video stream.

Abstract: A non-uniformity correction (NUC) calibration method comprises obtaining image data for a plurality of images with an image sensor, wherein each image in the plurality of images is obtained at a different respective global pixel gain setting and global expose in the image sensor; and using the image data for non-uniformity correction calibration to compute pixel NUC values for the pixels in the image sensor. The method can further include storing the pixel NUC values and obtaining further image data corrected by the stored pixel NUC values. In embodiments, the method can include moving a platform based on the further image data. In certain embodiments, the platform can be a guided munition.

Abstract: A method performed with an image sensor having a pixel array. At least one frame of a scene may be obtained using the pixel array. At least one region of interest (ROI) is identified within the frame. Subsequent frames of the scene are obtained, which involves controlling the pixel array to perform high resolution imaging with respect to the at least one ROI and low resolution imaging using analog binning with respect to remaining regions of the frames.

Abstract: A method may include obtaining sensor data from one or more LiDAR units and determining a point-cloud corresponding to the sensor data obtained from each respective LiDAR unit. The method may include aggregating the point-clouds as an aggregated point-cloud and generating an initial proposal for a two-dimensional ground model made of multiple grid blocks. The method may include filtering out unrelated raw data points from each grid block of the plurality of grid blocks to generate a filtered point-cloud matrix. The method may include identifying one or more surface-points and one or more object-points included in the filtered point-cloud matrix and generating an array of extracted objects based on the object-points.

Abstract: Image signal processing includes obtaining two or more image signals from a first hyper-hemispherical image sensor, where each of the two or more image signals has a different exposure and obtaining two or more image signals from a second hyper-hemispherical image sensor, where each of the two or more image signals has a different exposure. Image signal processing includes generating an exposure compensated image based on a gain value applied to an exposure level of a first image and a gain value applied to an exposure level of a second image. Image signal processing further includes performing high dynamic range (HDR) processing on the exposure compensated image. The HDR processing may be performed on a high a frequency portion of the exposure compensated image.

Abstract: A control apparatus includes a receiving unit that receives a captured image obtained by capturing an image in an optical finder of an image capture device in a state in which AF frames are displayed on the optical finder, a display unit that displays the captured image and a UI (user interface) for selecting at least one AF frame from AF frames displayed on the captured image, and a transmitting unit that transmits, to the image capture device, information for specifying the at least one AF frame selected via the UI.

Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).

Abstract: Devices, systems and methods for video processing are described. In an exemplary aspect, a method for video processing includes performing a conversion between a coded representation of a video comprising one or more video regions and the video. The coded representation includes first side information that provides a clipping parameter for filtering a reconstruction of a video unit of a video region using a non-linear adaptive loop filter, and wherein the first side information is signaled together with second side information indicative of filter coefficients used in the non-linear adaptive loop filter.

Abstract: An image processing method includes: receiving specific data of an image component of input image for a specific color encoding channel wherein the specific data of the image component is formed by the image component's multiple raw values having identical image characteristics; spatially classifying pixel units within the input image into a plurality of blocks according to a specific block size; for each block, obtaining values of the image component of pixel units within each block to calculate the average value of the image component of the each block; generating interpolated values of the image component of an interpolated image according to values of the image component corresponding to the plurality of blocks; and, blending the interpolated values of the image component of the interpolated image with original/raw values of the image component of the input image to generate an output image having modified values of the image component.

Abstract: A method, system, and computer program product for determining selection parameters for filtering algorithms using environmental data of images. The method may include receiving an image. The method may also include analyzing the image using at least image processing. The method may also include identifying, based on the analyzing, image data and environmental data of the image. The method may also include inputting the image data and the environmental data into a machine learning algorithm, where the machine learning algorithm includes mapped relationships between at least the environmental data and selection parameters. The method may also include predicting, using the machine learning algorithm, optimal selection parameters for the image. The method may also include applying the optimal selection parameters to a filtering algorithm for the image. The system and computer program product may include similar steps.

Abstract: Devices, systems and methods for video processing are described. In an exemplary aspect, a method for video processing includes performing a conversion between a coded representation of a video comprising one or more video regions and the video. The coded representation includes first side information that provides a clipping parameter for filtering a reconstruction of a video unit of a video region using a non-linear adaptive loop filter, and wherein the first side information is signaled together with second side information indicative of filter coefficients used in the non-linear adaptive loop filter.

Abstract: A distractor detector includes a heatmap network and a distractor classifier. The heatmap network operates on an input image to generate a heatmap for a main subject, a heatmap for a distractor, and optionally a heatmap for the background. Each object is cropped within the input image to generate a corresponding cropped image. Regions within the heatmaps that correspond to the objects are identified, and each of the regions is cropped within each of the heatmaps to generate cropped heatmaps. The distractor classifier then operates on the cropped images and the cropped heatmaps to classify each of the objects as being either a main subject or a distractor.

Abstract: Systems and method for correcting images including artifacts due to dirty camera lenses of electronic device are disclosed.

Abstract: A reading device includes a first light source configured to emit light, a second light source configured to emit light in a wavelength of light different from a wavelength of light of the first light source, circuitry configured to instruct the first light source and the second light source to turn on and turn off, a first light-intensity adjuster configured to adjust light intensity of the first light source based on a first light intensity value, a second light-intensity adjuster configured to adjust light intensity of the second light source based on a second light intensity value, a first image sensor configured to receive the light that is emitted from the first light source and is reflected by an object, and a second image sensor configured to receive the light that is emitted from the first light source and the second light source and is reflected by the object.

Abstract: Devices, systems and methods for video processing are described. In an exemplary aspect, a method for video processing includes performing a conversion between a coded representation of a video comprising one or more video regions and the video. The coded representation includes first side information that provides a clipping parameter for filtering a reconstruction of a video unit of a video region using a non-linear adaptive loop filter, and wherein the first side information is signaled together with second side information indicative of filter coefficients used in the non-linear adaptive loop filter.

Abstract: A method and a system are for image noise reduction. In an embodiment, the method includes producing a recorded image; establishing an amount of noise of the recorded image; decomposing the amount of noise into a number of N frequency-dependent noise components for N frequency bands, the number of N frequency-dependent noise components including respective data points respectively reproducing noise, of the amount of noise in the recorded image, for the respective frequency bands of the N frequency bands; examining the number of N frequency-dependent noise components for outlier data points, where an intensity lies outside a range of values, and forming moderated noise components by moderation of values of the outlier data points established in the examining of the number of N frequency-dependent noise components; and subtracting the moderated noise components from the recorded image.

Abstract: A cluster-based approach and template-based approach are combined to segment brain matter from a three-dimensional MRI image of voxels. The morphological information captured by the template-based approach may be used to refine the segmentation produced by the cluster-based approach. Conversely, the “similarity” information captured by the cluster-based approach may be used to refine the segmentation produced by the template-based approach.

Abstract: The present disclosure relates to image signal processing methods, apparatus, and devices. One example image signal processing method includes obtaining an image signal, where the image signal is derived based on a sensor signal collected by an image sensor, recognizing, by using a neural network, a scene to which the image signal belongs, determining, by using attribute information of the image signal, whether the scene is accurate, and in response to determining that the scene is accurate, performing enhancement processing on the image signal based on the scene to generate an enhanced image signal.

Abstract: Techniques are disclosed for panoramic image construction based on images captured by rotating imagers. In one example, a method includes receiving a first sequence of images associated with a scene and captured during continuous rotation of an image sensor. Each image of the first sequence has a portion that overlaps with another image of the first sequence. The method further includes generating a first panoramic image. The generating includes processing a second sequence of images based on a point-spread function to mitigate blur associated with the continuous rotation to obtain a deblurred sequence of images, and processing the deblurred sequence based on a noise power spectral density to obtain a denoised sequence of images. The point-spread function is associated with the image sensor's rotation speed. The second sequence is based on the first sequence. The first panoramic image is based on the denoised sequence.

Abstract: Examples associated with relationship preserving projection of digital objects are disclosed. One example includes identifying suitable locations within a physical space for projection of digital objects. The digital objects may be stored in a virtual space. A representation of a first digital object from the virtual space may be projected onto a first suitable location in the physical space and a representation of a second digital object from the virtual space may be projected onto a second suitable location in the physical space. The first suitable location and the second suitable location may preserve a spatial relationship between the first digital object and the second digital object.

Abstract: An image display method for preventing display colors from being distorted according to a viewing angle of a driver, and an image display method for a vehicle may include: tracking a sight line of a driver; determining an angle formed between a display and the sight line of the driver; loading a gain value corresponding to the determined angle from a prestored gain compensation table; applying the loaded gain value to image data displayed on the display; and displaying the image data to which the gain value has been applied on the display.

Abstract: Provided is an image generating device including a first lighting unit configured to emit light to an object, a second lighting unit configured to emit light to the object and located at a position different from a position of the first lighting unit, a light receiver configured to receive light reflected from the object when light is emitted to the object from the first lighting unit or the second lighting unit, and an image processor configured to obtain image data based on the light received by the light receiver and process the obtained image data, wherein the image processor obtains a first image based on light reflected from the object when light is emitted from the first lighting unit, obtains a second image based on light reflected from the object when light is emitted from the second lighting, generates a processed image by synthesizing the first image and the second image to correct a pixel value of a light noise region of the first image and/or the second image.

Abstract: A method of manufacturing a metal product comprise forming a base region on a surface of a metal member by repeatedly scanning along a predetermined first direction while irradiating the surface of the metal member with a base laser beam over a first set of rows. The method further comprises forming a marking by repeatedly scanning along a predetermined second direction while irradiating the surface of the metal member with a marking laser beam over a second set of rows. The second direction is different from the first direction. An identification code having a predetermined pattern comprises a combination of the base region and the marking.

Abstract: An image processing apparatus includes a memory and processing circuitry electrically coupled to the memory. The processing circuitry is configured to correct image data in a section of an abnormal image, based on information that indicates a position of the section in an abnormal line image that is included in a received image. The processing circuitry is configured to output the corrected image data.

Abstract: A method for denoising video content includes identifying a first frame block of a plurality of frame blocks associated with a first frame of the video content. The method also includes determining an average intensity value for the first frame block. The method also includes determining a first noise model that represents characteristics of the first frame block. The method also includes generating a denoising function using the average intensity value and the first noise model for the first frame block. The method further includes denoising the plurality of frame blocks using the denoising function.

Abstract: A method for processing a display image in an image display region, a display image processing device, a display device, and a storage medium are disclosed. A part of the image display region or all of the image display region is a movable region, and the method for processing the display image includes: in a case where the movable region is moved from a first position to a second position, obtaining a first image feature value of the display image displayed in the image display region where the movable region is at the first position; obtaining a second image feature value of the display image displayed in the image display region where the movable region is at the second position; and determining whether the display image displayed in the image display region is a static image based on the first image feature value and the second image feature value.

Abstract: A method implemented by a client device includes accessing a plurality of image frames captured by one or more cameras of the client device and generating a working image frame based at least in part on one or more of the plurality of image frames. The method further includes classifying one or more first objects detected in the working image frame based at least in part on a determined desirability of the one or more first objects. The one or more first objects are determined to be undesirable. The method further includes applying a pixel filtering process to the working image frame to replace one or more first pixel sets associated with the first objects with pixels from one or more image frames of the plurality of image frames to generate a final image frame, and displaying the final image frame on a display of the client device.

Abstract: Disclosed is an electronic device and a method to generate alpha masks of video frames in a video comprising a plurality of video frames including a first video frame and a second video frame following the first video frame, where the method comprising; obtaining a first alpha mask of the first video frame; providing a first downscaled video frame, wherein the first downscaled video frame is a lower resolution version of the first video frame; providing a first downscaled alpha mask of the first alpha mask; estimating a first primary coefficient and a first secondary coefficient based on the first downscaled video frame and the first downscaled alpha mask; and generating a second alpha mask for the second video frame based on the first primary coefficient and the first secondary coefficient.

Abstract: An object of the present invention is to provide a water level measurement device and a shoreline extraction method each of which is capable of stably measuring the water level. A pixel selection unit (11) selects a pixel of interest (302) from a designated area (301) designated from a captured image (300), and an identification image extraction unit (12) extracts identification images (303), (304) each coming in contact with the pixel of interest (302). The identification unit (13) calculates an identification strength indicating a degree to which an area corresponding to each of the identification images (303), (304) is a water area, on a basis of the result of machine learning related to identification between the water area and a non-water area.

Abstract: Images are accessed representing a status in a fabrication of a semiconductor chip corresponding to a particular stage in the fabrication. Distortion is removed from the images and actual features of the semiconductor chip are extracted from the images. Synthesized ideal features of the semiconductor chip associated with completion of the particular stage in the fabrication are determined from the one or more images. The actual features are compared to the ideal features to determine whether anomalies associated with the particular stage exist in the semiconductor chip.

Abstract: In a method for dithering image data, a processor receives an input image in a first color space and a first bit depth larger than 8 bits. The processor converts the input image into a first image in a perceptually uniform color space, such as the BT. 2100 ICtCp color space, with pixel values scaled to be within a target bit depth, it quantizes the first image to generate a quantized image, it generates an error image between the first image and the quantized image, and using a random threshold, it generates a dithered image based on the random threshold, the error image, and the quantized image, allowing to reduce the bit-depth representation for visually consistent reproduction by two bits.

Abstract: Methods, systems, devices and computer software/program code products enable generating reduced-noise image frames based on image frames received from a digital, camera pipeline; and enable efficient stereo image search between corresponding images generated by at least two cameras.

Abstract: Approaches presented herein can reduce temporal lag that may be introduced in a generated image sequence that utilizes temporal accumulation for denoising in dynamic scenes. A fast historical frame can be generated along with a full historical frame generated for a denoising process, with the fast historical frame being accumulated using an exponential moving average with a significantly higher blend weight. This fast history frame can be used to determine a clamping window that can be used to clamp a corresponding full historical value before, or after, reprojection. The fast historical blend weight can be adjusted to control the amount of noise versus temporal lag in an image sequence. In some embodiments, differences between fast and full historical values can also be used to determine an amount of spatial filtering to be applied.

Abstract: A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.