Abstract: A system and method for enhancing video resolution is disclosed. A Low-Resolution (LR) video including a plurality of LR images is received. The LR video is converted to a High-Resolution (HR) video by transforming at least one LR image to a HR image. Pixel unshuffle is performed to rearrange image portions in the LR image to generate a set of downsampled sub-images. Further, shallow features are extracted from the set of downsampled sub-image. The shallow features extracted corresponding to the set of downsampled sub-images configure a feature space. The shallow features are processed using a series of inverted residual blocks to generate enhanced features corresponding to the set of downsampled sub-images. The enhanced features are mapped to a high resolution feature space using pixel shuffle to generate the HR image corresponding to the LR image.

Abstract: A system, method, and computer program product are provided for exchanging images. In use, one or more images are received at a server. Additionally, the one or more images are analyzed. Further, image processing code is outputted referencing the one or more images, based on the analysis of the one or more images. Additional systems, methods, and computer program products are also presented.

Abstract: Systems and methods for image generation are provided. An aspect of the systems and methods for image generation includes obtaining an original image depicting an element and a target prompt describing a modification to the element. The system may then compute a first output and a second output using a diffusion model. The first output is based on a description of the element and the second output is based on the target prompt. The system then computes a difference between the first output and the second output, and generates a modified image including the modification to the element of the original image based on the difference.

Abstract: Embodiments described herein provide systems and methods for image editing, a first copy and a second copy of an input image are generated; noise is iteratively added to the first copy and the second copy by: updating the first copy based on a first inverted output of a denoising diffusion model (DDM) based on the second copy and a first caption and updating the second copy based on a second inverted output of the DDM based on the first copy and the first caption. A resultant noised image is iteratively denoised by a reverse process using the DDM conditioned on a second caption, thereby producing a final image.

Abstract: Disclosed are an image content extraction method and an image content extraction apparatus, a terminal, and a storage medium. The image content extraction method includes acquiring an image to be processed. Performing high-contrast retention on the image to be processed to obtain a high-contrast image of the image to be processed. Performing image fusion on the image to be processed and the high-contrast image to obtain a fused image. Performing linear light enhancement on the fused image to obtain a linear light-enhanced image. And using first pixel points, having pixel values located within a preset pixel value range, in the linear light-enhanced image as an image content of the image to be processed. The present disclosure can improve the integrity of image content extraction.

Abstract: The invention provides a method for adapting a text-to-image (T2I) diffusion model for video editing by using spectral decomposition to achieve controlled spectral shifts in the model's weights. This adaptation involves maintaining constant singular vectors while selectively adjusting singular values in response to a text prompt. A spectral shift regularizer constrains adjustments, particularly limiting changes to larger singular values to ensure minimal deviation from the original model's structure. This approach allows efficient, prompt-driven video editing by modifying specific elements according to the prompt while preserving the original video context. By focusing on selective spectral adjustments, the method reduces adaptation time and computational demands, making it suitable for real-time and resource-sensitive applications, such as dynamic video editing for streaming services.

Abstract: Apparatuses, systems, and techniques are presented to train and utilize one or more neural networks. A denoising diffusion generative adversarial network (denoising diffusion GAN) reduces a number of denoising steps during a reverse process. The denoising diffusion GAN does not assume a Gaussian distribution for large steps of the denoising process and applies a multi-model model to permit denoising with fewer steps. Systems and methods further minimize a divergence between a diffused real data distribution and a diffused generator distribution over several timesteps. Accordingly, various embodiments may enable faster sample generation, in which the samples are generated from noise using the denoising diffusion GAN.

Abstract: An array substrate includes a gate drive circuit including shift register units in cascade. The driving method includes: in a first driving mode, loading a first clock signal with an active level and an inactive level alternating periodically to a cascade clock signal terminal of a shift register unit, and loading a second clock signal with an active level and an inactive level alternating periodically to any one of K drive clock signal terminals of the shift register unit; and in a second driving mode, loading the first clock signal to the cascade clock signal terminal, and loading a third clock signal with an active level appearing within a target time period to a target drive clock signal terminal among the K drive clock signal terminals so that a target drive output terminal corresponding to the target drive clock signal terminal outputs a gate turn-on signal.

Abstract: An information processing device includes an extraction unit that extracts a core line from an image containing dermatoglyphic pattern; an identification unit that identifies an evaluation value indicating possibility of the dermatoglyphic pattern of the core line; and a correction unit that corrects the image based on the evaluation value.

Abstract: In a measuring device, a position of a laser scanner on a top plate (201) of a housing has been decided by pins (220) and a fixing member (210), and the laser scanner has been attached to the housing, and also the laser scanner that has been in a attached state can be detached, and again the position on the top plate (201) can be decided by the pins (220) and the fixing member (210), and the laser scanner can be attached to the top plate (201).

Abstract: Hybrid video decoder supporting intermediate view synthesis of an intermediate view video from a first- and a second-view video which are predictively coded into a multi-view data signal with frames of the second-view video being spatially subdivided into sub-regions and the multi-view data signal having a prediction mode is provided, having: an extractor configured to respectively extract, from the multi-view data signal, for sub-regions of the frames of the second-view video, a disparity vector and a prediction residual; a predictive reconstructor configured to reconstruct the sub-regions of the frames of the second-view video, by generating a prediction from a reconstructed version of a portion of frames of the first-view video using the disparity vectors and a prediction residual for the respective sub-regions; and an intermediate view synthesizer configured to reconstruct first portions of the intermediate view video.

Abstract: A method and device for generating a satellite remote-sensing image with high spatial, temporal and spectral resolutions is provided. The method includes: fusing a multispectral image with high temporal and spatial resolutions but a low spectral resolution and a hyperspectral image with a high spectral resolution but low temporal and spatial resolutions to generate a fused image with high spatial, temporal and spectral resolutions. Compared with a current spatial-temporal-spectral integrated fusion method, the proposed method is more in line with resolution characteristics of current mainstream satellite-borne remote-sensing data and can achieve high-fidelity fusion performance.

Abstract: The present disclosure relates to apparatus (100) for estimating depth of scene, comprising plurality of sensors (101) including Time of Flight (ToF) sensors and stereo sensors pairs, memory (103) and one or more processors (104). At least one pair of stereo sensors is placed perpendicular to other pairs of stereo sensors. One or more processors (104) are configured to determine disparity for plurality of sensors (101) based on comparison between one or more features of each image with other images received from plurality of pairs of stereo sensors, and evaluation of each image based on one or more pre-defined cost function parameters; determine disparity based on depth information received from ToF sensors; determine confidence value for each of plurality of sensors (101), based on disparity, to generate plurality of confidence values and estimate depth of scene based on disparity associated with a sensor selected based on plurality of confidence values.

Abstract: A method with image processing includes: determining a feature map of an image; determining a spatial position weight of pixel points of the feature map; determining a corrected feature map by correcting the feature map based on the spatial position weight of the pixel points; and determining a key point based on the corrected feature map.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”).

Abstract: A video stream processing method and apparatus are provided. The method includes obtaining an image set of a target person from a multi-channel video stream, the multi-channel video stream being obtained for a same scene by a plurality of cameras, and an image in the image set includes a front face image of the target person; determining a virtual viewpoint in a target-person view mode based on the image in the image set; and projecting, based on a depth map of a target image and a pose of a real viewpoint corresponding to the target image, the target image onto an imaging plane corresponding to the virtual viewpoint to obtain a video stream in the target-person view mode, the target image intersecting with a vision field of the target person in the multi-channel video stream.

Abstract: A camera actuator is configured to include: a fixed-side member; a movable-side member disposed separated from the fixed-side member in a direction orthogonal to a light axis, the movable-side member holding a lens unit; a first lens drive actuator that displaces the movable-side member in at least a prescribed direction; an elastic support member that extends in the direction orthogonal to the light axis, and supports the movable-side member in relation to the fixed-side member so as to allow the displacement in at least the prescribed direction; and a damping member that is disposed between the movable-side member or a member fixed to the movable-side member, and the fixed-side member or a member fixed to the fixed-side member.

Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for image processing. In some embodiments, a method of image processing includes acquiring, from a user, a first image. The method further includes removing, using an image de-filter network, a filter effect applied to the first image to generate a second image. The method further includes obtaining, based on the first image and the second image, an image filter corresponding to the filter effect. The method further includes rendering a third image using the obtained image filter to output a fourth image.

Abstract: A computer-implemented method for compressing computer-readable data having improved efficiency can include obtaining, by a computing system including one or more computing devices, input data associated with the computing system; and encoding, by the computing system, the input data and added noise from a noisy channel to produce encoded data based at least in part on an encoder model, wherein encoding the input data and added noise includes additively combining the added noise and the input data to obtain noisy input data and rounding the noisy input data by a soft rounding function, the soft rounding function having a sharpness, to produce the encoded data, wherein the machine-learned encoder model is trained on training data, wherein the training data is encoded with the added noise from the noisy channel.

Abstract: A method for managing image data in an automotive lighting device. This method includes the steps of providing an image pattern, dividing the image pattern in rows or columns of pixels, and providing for each row pattern, a plurality of linear segments, each linear segment is characterized by two values. The data of the linear segments is compressed, and the compressed data is sent to the light module. The invention also provides an automotive lighting device for performing the steps of such a method.

Abstract: A method and system including: defining a geographic area; receiving a plurality of images; determining a plurality of image points; partitioning the geographic area into a plurality of image regions based on the plurality of image points; and stitching the plurality of images into a combined image based on the plurality of image regions.

Abstract: An image processing method and a device configured to implement the same are disclosed. The method comprises: obtaining optical input from a hybrid imaging device, wherein an obtained optical input comprises a first component and a second component that temporally corresponds to the first component; wherein the first component of the obtained optical input corresponds to a first temporal resolution, while the second component of the obtained optical input corresponds to a second temporal resolution higher than that of the first component; performing image restoration operation on a first subset of the first component of the obtained optical input in accordance with data from the second component of the obtained optical input; and performing image fusion operation to generate fused image data from an output of the image restoration operation and a second subset of the first component of the obtained optical input.

Abstract: System, device, and method for improved encoding and enhanced compression of images, videos, and media content. A method includes: (a) receiving a source image, and analyzing its content on a pixel-by-pixel basis, and classifying each pixel as either (I) a pixel associated with Photographic content, or (II) a pixel associated with Non-Photographic content; (c) generating a pixel-clusters map that indicates (i) clusters of pixels that were classified as Photographic content, and (ii) clusters of pixels that were classified as Non-Photographic content; (d) generating a composed image, by: (d1) applying a first encoding technique, particularly lossy encoding, to encode pixel-clusters that were classified as Photographic content; (d2) applying a second, different, encoding technique, particularly lossless encoding, to encode pixel-clusters that were classified as Non-Photographic content.

Abstract: A method for acquiring motion vectors includes: obtaining a number of related motion vectors of a current block, wherein the number of related motion vectors are motion vectors of a related block of the current block, a prediction mode of the related block is the same as a current prediction mode of the current block, an encoding has been completed; determining weights of at least part of the number of related motion vectors; and calculating a weighted motion vector of the current block based on at least part of the number of related motion vectors and the weights of at least part of the plurality of related motion vectors.

Abstract: A system or process may generate a summarization of multimedia content by determining one or more salient moments therefrom. Multimedia content may be received and a plurality of frames and audio, visual, and metadata elements associated therewith are extracted from the multimedia content. A plurality of importance sub-scores may be generated for each frame of the multimedia content, each of the plurality of sub-scores being associated with a particular analytical modality. For each frame, the plurality of importance sub-scores associated therewith may be aggregated into an importance score. The frames may be ranked by importance and a plurality of top-ranked frames are identified and determined to satisfy an importance threshold. The plurality of top-ranked frames are sequentially arranged and merged into a plurality of moment candidates that are ranked for importance. A subset of top-ranked moment candidates are merged into a final summarization of the multimedia content.

Abstract: An image processing apparatus performs external recognition for driving assistance or automated driving of a vehicle based on a captured image obtained from a photographing apparatus capturing an image requiring distortion correction. The image processing apparatus: moves and sets a partial region to be a target of distortion correction processing according to a rule, and applies the distortion correction processing to the set partial region of the captured image to acquire a partial image corrected in distortion; detects a road mirror around the vehicle; and changes the rule so that a partial region corresponding to a predetermined direction of the vehicle is preferentially set as a target of the distortion correction processing when a road mirror is detected.

Abstract: Systems, apparatuses, and methods are described for 3D luminescence imaging, by identifying a preferred optical pair and optimizing a scanned image using the preferred optical pair. An optimal filter pair may be selected from a list of two or more optical filters. An acceptable threshold of information may be obtained using a subset of the list of two or more optical filters (e.g., an optimal filter pair). An imaging device may be configured with the optimal filter pair to produce a pair of luminescence images of a target sample. In addition, luminescence images may be pre-processed to reduce the time-cost of conventional processing techniques of luminescence images. One or more computing devices may generate initial prior data based on a pair of luminescence images. An output may include one or more output luminescent sources that have been refined and/or optimized from the initial prior data.

Abstract: An image processing method and apparatus, an electronic device, and a storage medium, relating to the technical field of image processing. The method includes: acquiring a first image and a second image, wherein a resolution of the second image is greater than a resolution of the first image; determining difference information between a target pixel in the second image and a reference pixel corresponding to the target pixel point in the first image; and acquire a target image with the same resolution as the second image by applying an image differencing process to a predetermined image with the same resolution as the first image based on the difference information. This method can obtain the target image based on the difference value information, and improve image quality.

Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: A multi-scale image processing algorithm for enhancing contrast in an electronic representation of an image represented by a) decomposing said digital image into a set of detail images at multiple resolution levels and a residual image at a resolution level lower than said multiple resolution levels, b) processing at least one pixel of said detail images, c) computing a processed image by applying a reconstruction algorithm to the residual image and the processed detail images, said reconstruction algorithm being such that if it were applied to the residual image and the detail images without processing, then said digital image or a close approximation thereof would be obtained.

Abstract: An image processing method includes: acquiring a first image containing a target object; inputting the first image into an image processing model to obtain a second image, the second image being a mask image of the target object in the first image, a value for each pixel in the second image being in a range of 0 to 1, inclusive ([0, 1]), and the range of 0 to 1, inclusive ([0, 1]) indicating a degree of relation between each pixel in the second image and a pixel in the target object; fusing the first image and a background image according to the second image to obtain a fused image; and providing a first interface and displaying the fused image on the first interface.

Abstract: Layouts for elements of a flowchart or flow diagram may use recursive algorithms that trace along each parent-child pathway to ensure that child elements always occur after parent elements in a horizontal spacing. Vertical spacing requirements may then be calculated for each element, along with a vertical space above a center line and a vertical space below a center line for each element. These vertical spacing requirements may then be used by a recursive algorithm to assign vertical positions to each of the elements. The vertical spacing may ensure that lanes created for each branch element are not crossed by later elements in the process flow, which ensures that connections between elements may be clearly distinguished from each other.

Abstract: Provided is a learning device that acquires computational imaging information of a computational imaging camera that captures an image with blurring; acquires a normal image captured by a normal camera that captures an image without blurring or an image with blurring smaller than that of the computational imaging camera, and a correct answer label assigned to the normal image; generates an image with blurring based on the computational imaging information and the normal image; and performs machine learning using the image with blurring and the correct answer label to create an image identification model for identifying an image captured by the computational imaging camera.

Abstract: A computer-implemented method is provided. The computer-implemented method includes inputting a low-resolution image and a plurality of high-resolution images into a feature extractor, the low-resolution image and the plurality of high-resolution images including images of a target object; obtaining, by the feature extractor, feature maps of the low-resolution image and the plurality of high-resolution images; comparing similarities between the feature maps of the low-resolution image and the plurality of high-resolution images; obtaining selected feature maps of one or more selected high-resolution images of the plurality of high-resolution images most similar to the low-resolution image; inputting the selected feature maps into a generator to output a repair image; enhancing the low-resolution image using a pre-processing image enhancing process to generate an enhanced image; and morphing the repair image with the enhanced image.

Abstract: A method for defending against an adversarial sample in image classification includes: denoising, by an adversarial denoising network, an input image to acquire a reconstructed image; acquiring, by a target classification model, a predicted category probability distribution of the reconstructed image; acquiring, by the target classification model, a predicted category probability distribution of the original input image; calculating an adversarial score of the input image, and determining the input image as an adversarial sample or a benign sample according to a threshold; outputting a category prediction result of the reconstructed image if the input image is determined as the adversarial sample; and outputting a category prediction result of the original input image if the input image is determined as the benign sample. A system for defending against an adversarial sample in image classification, and a data processing terminal are further provided.

Abstract: An arithmetic part of an arithmetic processing device includes: a filter processing part that has a multiplier and a first adder and performs filter processing; a second adder that performs cumulative addition processing that cumulatively adds all of the results of the filter processing as executed in N parallel; a non-linear conversion part that performs non-linear arithmetic processing on the result of the cumulative addition processing; a pooling processing part that performs pooling processing on the result of the non-linear arithmetic processing; and an arithmetic control part that controls the filter processing part, the second adder, the non-linear conversion part, and the pooling processing part.

Abstract: An image processing method includes displaying a first key frame image obtained from a sequence frame video stream; obtaining a first foreground image and a first background image obtained after foreground and background separation processing is performed on the first key frame image; obtaining a second foreground image obtained after foreground image processing is performed on a first target object in the first foreground image; obtaining a third background image obtained after background repair processing is performed on the first background image using a second background image, where the second background image is a background image included in a second key frame image obtained by photographing a target scene before the first key frame image is obtained; and obtaining a first key frame image obtained after foreground and background compositing processing is performed on the second foreground image and the third background image.

Abstract: A system and method are disclosed for low-light image enhancement using denoising preprocessing with wavelet decomposition AI-based techniques to enhance image quality of low-light images. Subsampled images are created from a raw input image. A wavelet decomposition process is performed on each subimage to create multiple frequency domain subimages. Each frequency domain subimage is input into a corresponding neural network. The output of each corresponding network is input to an inverse wavelet module. The output of the inverse wavelet module is a denoised image that is input to an image signal processing pipeline, where additional processing may be performed on the denoised image.

Abstract: This application discloses an image processing method and apparatus, and relates to the field of image processing technologies, to help optimize a color, a contrast, or a dynamic range of an image, so that an optimized image can be more objective, and robustness is improved. The method is applied to a terminal including a first camera and a second camera. The method includes: when an ISO of the first camera in a current photographing environment is greater than a first threshold, capturing a first image for a first scenario in the current photographing environment; capturing a second image for the first scenario; and optimizing the first image based on the second image to obtain a third image. An image style of the second image is better than that of the first image.

Abstract: Image captured through a non-rectilinear lens may exhibit distortions. The distortions may be reduced by warping the image. However, warping the image may degrade the fidelity of the image. The warped image may be enhanced to increase the fidelity of the image. The enhancement may be applied to the portions of the image that were degraded from the warping.

Abstract: Machine vision techniques for determining a region of interest (ROI) are disclosed herein. An example implementation includes a computing device for executing an application, the application operable to: (1) receive a first image; (2) set an first ROI of the first image to a field of view (FOV) of the first image; (3) determine a barcode within the first ROI; (4) determine a bounding box of the barcode; (5) form a second ROI based on the bounding box; (6) receive a second image; and (7) set an ROI of the second image to be the second ROI of the first image.

Abstract: There are a medical image processing apparatus and a medical image processing method that are capable of maintaining the estimation accuracy of noise intensity even though a system noise ratio rises. A medical image processing apparatus includes a difference image generating unit that divides projection data obtained by applying radiation to an examinee to create a difference image between tomographic images reconstructed for every divided piece of projection data; a local variance computing unit that computes local variance in the difference image; and a noise estimation unit that corrects the local variance using a correction function found in advance to estimate noise intensity of a tomographic image of the examinee. The correction function includes system noise.

Abstract: Methods, systems, and apparatuses for adaptive processing of video content to remove noise, such as film grain noise, without substantially affecting visual presentation quality are described herein. A computing device may determine a plurality of film grain parameters associated with film grain noise present within one or more portions of a content item. The computing device may determine at least one encoding parameter based on the plurality of film grain parameters. The computing device may encode the content item based on the at least one encoding parameter. The computing device may send an encoding message to at least one user device/client device, which may in turn use the encoding message to decode the content item.

Abstract: One or more computing devices, systems, and/or methods for defect detection are provided. An image, depicting an object for evaluation to determine whether the object has a defect, is inputted into a segmentation model to identify an object region of interest of the object. An object region area of the object region of interest is calculated. A convex hull area of a convex hull encompassing the object region of interest is calculated. A ratio of the object region area to the convex hull area is determined. The ratio is compared to a threshold to determine whether the object has the defect or does not have the defect.

Abstract: A rotating wind turbine blade optical inspection devices and systems. The system for inspection, having: a camera; a rotatable device designed to controllably rotate camera along axis, not camera's lens axis of rotational symmetry; a laser distance sensor or a dynamic vision sensor and a computer system; the camera, the rotatable device and the laser distance sensor or the dynamic vision sensor operably connected with the computer system; wherein the camera is adapted to be mounted on the rotatable device; the distance or the vision sensors' optical axis is directed at a set distance from the camera's optical axis; wherein the computer system: (i) to receives a signal from the distance or vision sensors; (ii) to determines camera's activation time based on the signal from the distance sensor or vision sensors and a pre-set adjustable delay time; (iii) to activate the camera acquiring an image of a blade area inspected.

Abstract: An apparatus includes a processing node of a distributed computing platform. The processing node communicates with other processing nodes over one or more networks. The processing node may receive frames of point clouds at a processing node of a distributed computing platform, determine a subset of the frames as key frames based at least in part on distances travelled between captures of the respective frames, and allocate tasks of processing the key frames to processing subnodes based at least in part on estimated processing demands of the key frames and processing capabilities of each of the processing subnodes.

Abstract: A structural analysis computing device may generate a proposed insurance claim and/or generate a proposed insurance quote for an object pictured in a three-dimensional (3D) image. The structural analysis computing device may be coupled to a drone configured to capture exterior images of the object. The structural analysis computing device may include a memory, a user interface, an object sensor configured to capture the 3D image, and a processor in communication with the memory and the object sensor. The processor may access the 3D image including the object, and analyze the 3D images to identify features of the object—such as by inputting the 3D image into a trained machine learning or pattern recognition program. The processor may generate a proposed claim form for a damaged object and/or a proposed quote for an uninsured object, and display the form to a user for their review and/or approval.

Abstract: Disclosed are a positioning method, an electronic device, and a storage medium. The method includes: converting, according to an intrinsic parameter matrix of an image acquisition module and depth values of multiple pixel points in a depth image captured by the image acquisition module, pixel coordinates of the multiple pixel points into camera coordinates; and matching the camera coordinates of the respective pixel points with target world coordinates of multiple voxels in a pre-established point cloud map, and obtaining a positioning result of the image acquisition module; wherein the target world coordinates of the respective voxels are obtained by updating initial world coordinates of the respective voxels according to multiple sample image pairs, and a sample image pair comprises a two-dimensional sample image and a depth sample image.

Abstract: An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.

Abstract: A display device including a display; one or more processors; and a memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for receiving an input image to be displayed on the display; selecting a look-up table (LUT) curve stored in the memory for the received input image; performing a histogram on the received input image; applying an offset gain to the selected LUT curve based on the histogram to generate an offset LUT curve; performing tone mapping of the input image through the offset LUT curve; and displaying the tone mapped image on the display.