689 results about "Intermediate image" patented technology

A method includes determining at an extended reality (XR) device, a first set of vertex adjustment values of a distortion mesh and receiving image frame data of a scene captured at a first time and at a first head pose using a see-through camera of the XR device. The method further includes applying the first set of vertex adjustment values of the distortion mesh to the image frame data to obtain intermediate image data, and predicting a second head pose at a second time subsequent to the first time. The method also includes generating, based on the predicted second head pose, a second set of vertex adjustment values of the distortion mesh, applying the second set of vertex adjustment values of the distortion mesh to the intermediate image data to generate a rendered virtual frame and displaying the rendered virtual frame by the XR device at the second time.

A method, apparatus, non-transitory computer readable medium, and system for video generation include obtaining an input image having an element depicted in a first view angle, generating a synthetic image depicting the element of the input image from a second view angle different from the first view angle, generating an intermediate image by interpolating based on the synthetic image, and generating a video based on the synthetic image and the intermediate image, where the video depicts the element of the input image from a changing view angle.

Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.

The invention discloses a four-channel fluorescence detection light path module of a super-resolution biological microscope, which divides a fluorescence signal of an intermediate image plane (5) into four conjugate channels (7a-7d) through multi-stage dichroscope light splitting, reflector guiding and lens focal plane adjustment on the premise of not introducing additional aberration, and realizes real-time synchronous detection by using a single large-target-surface camera (8). By adjusting the distance between the reflectors, the inclination angle of the parallel plate glass or the position of the lens, the device can adapt to different camera target surface sizes and compensate chromatic aberration, and the problems of motor switching delay and dual-channel aberration in the prior art are solved.

The invention belongs to the technical field of image processing, and particularly relates to a bridge pier intelligent inspection method, which comprises the following steps of: performing Gaussian filtering on an inspection image through Gaussian filters with different scales, and subtracting an original inspection image from obtained background images with different scales to obtain intermediate images with different scales; calculating the enhancement degree of the gradient features of the pixel points in the intermediate images under different scales compared with the gradient features of the pixel points in the original inspection image; calculating the retention degree of the multiple LBP features of the pixel points in the intermediate image under different scales compared with the original inspection image; and according to the enhancement degree and the retention degree, calculating the weights of the gray values of the pixel points in the intermediate image under different scales, and carrying out weighted summation on the gray values of the pixel points in the intermediate image under different scales so as to obtain a uniform light image. According to the invention, the finally obtained uniform light image achieves better balance in detail and texture aspects.

The invention provides an image protection method and device based on a diffusion model, equipment and a medium, and relates to the technical field of face privacy security protection, and the method comprises the steps: carrying out the feature extraction of an obtained target image through employing a BLIP-2 model, obtaining a target image feature vector, a text description, and a text feature vector corresponding to the text description, the method comprises the following steps: obtaining a target image feature vector, taking text description as a private key prompt word Prompt1, inputting the target image feature vector and the text feature vector into a target comparison learning model, updating the private key prompt word Prompt1, obtaining an intermediate image output by a sending end, and transmitting the intermediate image and the updated private key prompt word Prompt1 to a receiving end. And the controllability of the target image in the encryption process and the security of the target image in the transmission process are improved.

A multimodal microscope having a light source for transmitting excitation light, an illumination beam path having a microscope objective for guiding the excitation light onto and / or into a sample, at least one first spatial light modulator being present in the illumination beam path for manipulating the excitation light, a detector for detecting detection light emitted by the sample owing to illumination with the excitation light, a detection beam path, comprising the microscope objective or a further microscope objective, for guiding the detection light onto the detector, and a controller at least for controlling the first spatial light modulator. The multimodal microscope includes a switchable optical functional group, which is switchable at least into a first and a second switching state, and the first spatial light modulator is situated either in or in the vicinity of an intermediate image plane or a pupil plane of the illumination beam path, depending on the switching state.

The invention discloses a machine vision-based automatic identification and calculation method for pore change of a rock section under an SEM (scanning electron microscope), and the method comprises the steps: obtaining original image data of the rock section through a scanning electron microscope, transmitting the original image data to a machine vision SEM pore intelligent quantification cloud platform, and dividing the original image data into a plurality of image sub-regions which are not overlapped with each other; calling a special threshold inversion model to analyze the gray information of each sub-region, and determining a dual-phase pore segmentation threshold; carrying out edge extraction on the sub-regions after threshold processing by adopting a pore edge gradient enhancement algorithm; performing topological structure analysis on the intermediate image data by using a biphase pore topological inversion model, constructing and optimizing a pore space topological network, and eliminating false pore structure data; and according to the optimized pore space topology network, integrating calculation results of all the sub-regions to obtain a pore change identification calculation result of the whole rock section. The method improves the pore identification precision and processing efficiency, and adapts to different types of rock samples.

The subject technology receives an input image, the input image comprising a selfie. The subject technology transforms, using a neural network, the input image to a latent representation of an identity. The subject technology transforms, using a diffusion model, a text condition to a second latent representation compatible with the latent representation of the identity. The subject technology transforms a pose template to a set of latent features for the diffusion model. The subject technology generates an intermediate image based on the latent representation of the identity, the second latent representation, and the set of latent features. The subject technology modifies, using a face enhancement network, the intermediate image based on the input image. The subject technology generates, using a face restoration network, a final output image based on the modified intermediate image. The subject technology provides for display the final output image on a display of a client device.

A method for digitizing image-based data includes receiving an image file including one or more target objects, generating an intermediate image by removing noise from the image file using a denoising machine learning model, identifying the one or more target objects included in the intermediate image using an object segmentation machine learning model, discretizing the one or more target objects that were identified using the trained object segmentation machine learning model, and storing the one or more target objects that were discretized in a data file, visualizing the one or more target objects, or both.

The invention discloses a lightweight high-speed photographing system and method based on an event camera, and the system comprises a hybrid imaging module which is used for synchronously collecting the image data and event data of a scene, and comprises a visible light camera and an event camera; the hardware synchronization module is electrically connected with the visible light camera and the event camera, and is used for receiving a frame synchronization signal of the visible light camera and sending a trigger signal to the event camera, so that the event camera inserts a time stamp corresponding to a visible light image frame exposure moment in an event stream; the data processing and fusion unit is in communication connection with the hybrid imaging module and is used for receiving the image data and the event data and executing the following steps: performing time alignment on an event stream and a visible light image frame stream based on a time stamp; operating a high-frequency frame insertion reconstruction algorithm, and reconstructing a multi-frame intermediate image by using event stream data between two frames; synthesizing the original visible light image frame and the reconstructed intermediate image into a high-frame-rate video stream; and the power supply and interface module is used for supplying power to the system and outputting a high-frame-rate video stream.

2.1. The simultaneous acquisition of multiple images in the microscope should be made possible with greater flexibility. Furthermore, simultaneous acquisition should be made possible with less effort. 2.2. In a microscope (1) with a main beam splitter (22) and an adjustable beam deflection unit (21) for moving the illuminating light through the sample space (P), the beam deflection unit (21) is arranged optically between the light source (12) and the main beam splitter (22) for this purpose, so that the sample light reaches the sensor (28) away from the beam deflection unit (21), wherein both the illuminating light and the sample light pass through the same intermediate image (ZB).

The invention relates to a cross-modal retrieval technology, in particular to a cross-modal image-text pedestrian retrieval method based on a generative model, which comprises the following steps: acquiring text description information and a corresponding original pedestrian image; calling a diffusion generation model to generate an intermediate image based on the text description; original image features and text features are extracted through an image encoder and a text encoder respectively, and semantic-fused text feature representation is constructed; calculating the similarity between the image feature representation and the text feature representation to obtain an image-text matching score; introducing and generating an intermediate image as a semantic bridge, and realizing multi-modal fusion among the image, the text and the intermediate image through a cross attention mechanism to obtain fused image and text features; and using the fusion features to train a recognition model, and finally outputting a pedestrian image retrieval result corresponding to the text description. According to the method, the image-text alignment precision can be improved, the robustness of a scene with incomplete text description is enhanced, and the recognition accuracy in a cross-modal retrieval task is improved.

The invention provides an image enhancement method, system and device for an automatic driving vision task, and belongs to the technical field of combination of computer vision and automatic driving, and the method comprises the steps: obtaining a multi-view image of a to-be-detected target, and splicing adjacent view images; applying a first size grid mask on the spliced image; removing a mask in an overlapping region of the spliced image to obtain an intermediate image; detecting a preset target area in the intermediate image, and applying a second size grid mask to the preset target area; and replacing the first size grid mask at the corresponding position with the second size grid mask to obtain an enhanced image. Based on the method, the invention further provides an image enhancement system for the automatic driving vision task. According to the method, a multi-view grid mask enhancement method is adopted, the first-size mask and the second-size mask are used for carrying out global subject covering and local small target covering respectively, and the robustness and generalization ability of a visual perception model in an automatic driving system in a complex environment are improved.

Disclosed are systems, apparatuses, processes, and computer-readable media for processing one or more images. For example, a method includes obtaining an inpainted image based on providing a first image to an ML model; combining the first image and a first auxiliary image of the first image into an intermediate image; obtaining an inpainted intermediate image based on providing the intermediate image to the ML model; and generating a second auxiliary image from the inpainted image and the inpainted intermediate image.

A method, apparatus, non-transitory computer readable medium, and system for generating images includes obtaining an image generation prompt and a reference prompt. The image generation prompt includes a first element and a second element and the reference prompt includes the second element. Embodiments then generate, using an image generation model, an intermediate image based on the reference prompt. Subsequently, embodiments generate, using the image generation model, a synthetic image including the first element and the second element based on the intermediate image and the image generation prompt.

Provided in the present disclosure are an image generation method and apparatus, an image processing model training method and apparatus, an image processing method and apparatus, and an electronic device and a storage medium, which can be applied to the fields of computer technologies, deep-learning technologies and image processing technologies. The image generation method comprises: on the basis of an initial noise image and an image category, determining a first noise image; on the basis of the initial noise image and the first noise image, determining a first intermediate image; and on the basis of the first intermediate image, generating a first target image that belongs to the image category.

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

A telecentric charged particle beam, which can be an electron beam, is directed through an array of apertures to divide the telecentric charged particle beam to a plurality of telecentric illumination beamlets. The telecentric illumination beamlets through an imaging lens, a field lens at an intermediate image plane, and a transfer lens. The telecentric illumination beamlets are scanned as the telecentric illumination beamlets are directed through an upper scan deflector and a lower scan deflector and directed onto a workpiece on a stage.

The invention relates to the technical field of image processing, and provides a construction site image processing method, device and equipment, and a medium, which can identify a non-target object in a target construction site image, generate a mask in the target construction site image according to an identification result, roughly position the non-target object, and generate a mask in the target construction site image according to the identification result. Accurate target segmentation is carried out according to positioning, so that the processing efficiency and precision can be effectively balanced; the target construction site image is erased according to the mask to obtain a first intermediate image with a blank area, and content completion is performed on the blank area based on an environment perception algorithm to obtain a second intermediate image, so that the problem of feature matching failure caused by dynamic interferents can be solved, and meanwhile, the structural integrity of a building main body is ensured; and detail reduction processing is performed on the second intermediate image based on the spatial semantic information of the building information model, so that the influence of illumination variation and shielding interference can be reduced, and a high-quality image which is more suitable for executing a construction site monitoring task is generated.

The invention relates to the technical field of agricultural unmanned aerial vehicle data processing, in particular to an agricultural unmanned aerial vehicle pod sensor data processing method and system which are used for correcting image data abnormity caused by spraying operation. Synchronously acquiring image data acquired by an optical sensor and vibration data acquired by a vibration sensor and generated by the spraying system during working; determining a characteristic vibration frequency according to the vibration data, and performing frequency domain filtering processing on the image data based on the characteristic vibration frequency to generate intermediate image data of which the vibration blur is eliminated; by synchronously acquiring the image and the vibration data, correcting the vibration blur by using the vibration data, identifying and confirming the lens liquid drop by using the spectral characteristics and the position fixity, and generating the mask layer to shield the liquid drop area, the problems of missed judgment and misjudgment of the liquid drop as a disease caused by the vibration blur are solved.

Optical coherence tomography angiography (OCTA) imaging is vulnerable to artifacts induced by eye motion, including displacement artifacts, duplicated scanning artifacts and white line artifacts. A two-stage framework for sequentially removing the three types of artifacts in a degraded OCTA image is developed. In the first stage, a hierarchical transformer model, named as HFormer, utilizes global features, vertical-line features and intra-vertical-line features of the degraded OCTA image to remove the displacement artifacts and duplicated scanning artifacts to thereby yield an intermediate OCTA image. In the second stage, a residual conditional diffusion model (RCDM) removes the white line artifacts by reconstructing an artifact region of the intermediate OCTA image overlapping with the white line artifacts while avoiding modifying a residual region of the intermediate OCTA image not overlapping with the white line artifacts.

An optical system is configured to guide light from a light source to a first target and a second target, and to guide first reflected light from the first target and second reflected light from the second target to an image sensor. The optical system includes a first optical element having a conical surface. An intermediate image is formed between the first optical element and each of the first target and the second target. The first reflected light and the second reflected light are guided to an imaging plane of the image sensor such that at least part of the first reflected light and part of the second reflected light overlap each other on the imaging plane.

An optical assembly (17) has a primary optical rod (28) for guiding illumination light (4) along an illumination light beam path from a light source to a target field. An optical follower rod (29) of the optical assembly (17) is arranged in the illumination light beam path between the primary rod (28) and the target field. An intermediate image-generating optic (30) is arranged between the primary rod (28) and the follower rod (29). This generates an intermediate image between the primary rod (28) and the follower rod (29) along exactly one imaging dimension (y) perpendicular to the illumination light beam path. The result is an optical assembly in which the illumination light, after passing through the optical assembly, has a defined polarization state. The optical assembly can be part of an illumination optic and can, in particular, be part of an optical system of a projection exposure system containing such an illumination optic.

The invention discloses an image reconstruction method and system, a terminal and a storage medium, and the method comprises the steps: reconstructing a limited angle image through a reconstruction algorithm, and obtaining an intermediate image; using a registration algorithm to align prior information to the intermediate image to obtain registration prior information; and processing the limited angle image based on the registration prior information to obtain a reconstructed limited angle image. According to the method, the prior information with similar structures is introduced, and information fusion is realized by means of registration, so that the problems of artifacts and fuzziness caused by incomplete data in limited angle imaging are effectively solved, the quality and stability of image reconstruction are improved, and the method has good clinical applicability and engineering application value.

The invention provides an unsupervised low-dose photon counting CT (Computed Tomography) reconstruction method and an unsupervised low-dose photon counting CT reconstruction device based on optimal transmission. According to the method, a low-dose photon counting CT reconstruction problem is modeled as a Cantrovitch problem under a distribution consistency constraint, and the method comprises the following steps: constructing and training an optimal transmission-based unsupervised projection recovery network and constructing and training an optimal transmission-based unsupervised image noise reduction network; performing detail recovery on the photon counting CT low-dose projection by using the trained unsupervised projection recovery network to generate a denoised projection; reconstructing the denoised projection by using an image reconstruction algorithm to generate an intermediate image; and performing noise reduction on the intermediate image by using the trained unsupervised image noise reduction network to obtain a final de-noised image. According to the method, the problem that a high-quality pairing data set is difficult to obtain and a photon counting CT low-dose image is difficult to reconstruct can be solved.

The present disclosure performs image in-painting with controlled text generations to overcome the challenges persisting in traditional diffusion-based methods, especially when it comes to generating textual content within the image with complex font attributes. In the present disclosure, initially, an input image and a textual prompt and a plurality of control parameters are given as input to the present disclosure. Further, character mask and conditional mask are extracted based on the inputs. Finally accurate customized textual images are generated based on the character mask and the conditional mask using a textual image generating diffusion model. The textual image generating diffusion model generates an intermediate image based on the input image and the random gaussian noise. This intermediate image is iteratively refined to generate a latent vector image, and the accurate customized textual image is generated from the latent vector image using a trained customized character map-guided consistency model.

System and method for Generative Artificial Intelligence based image generation are disclosed. In an aspect, a user input is received requesting an image of a predetermined object to be generated. An enhanced prompt is then generated from the user input, the enhanced prompt includes one or more of a reference image of the predetermined object and a textual portion describing technical details for generating the image. Further, the enhanced prompt is input to an image generation model that preserves portions of the reference image including the predetermined object unchanged via masking the portions of the reference image, generates a depth map of the reference image, and generates an intermediate image by merging the reference image with the masked portions of the predetermined object and the depth map. Also, an enhanced version of the intermediate image is output as the image generated in response to the user input.

A computer-implemented method for determining an estimation of a two-dimensional scattered radiation distribution in a two-dimensional X-ray image that is based on an imaging procedure performed by an X-ray detector is provided. The method includes receiving the X-ray image, applying an estimation algorithm to the X-ray image or to a two-dimensional intermediate image determined from the X-ray image. The estimation algorithm determines a respective two-dimensional partial scattered radiation image for a plurality of physical scatter processes such that image values of pixels of the respective partial scattered radiation image in each case describe an estimated value for a respective scattered radiation dose that was applied to a respective detector region of the X-ray detector assigned to the respective pixel during acquisition of the X-ray image by the respective physical scatter process. The estimation of the scattered radiation distribution is determined based on the plurality of partial scattered radiation images.

The invention relates to the technical field of image processing, in particular to a double-stage image restoration method for composite degradation, which comprises the following steps of: S1, inputting a degraded image into a restoration module to generate an intermediate image without degradation; the recovery module comprises an encoder and a decoder, the encoder adopts a Transform block to process cross-scale features in a spatial domain, the decoder adopts a WaveTrans block to recover image resolution, and composite degradation is removed in a frequency domain; s2, inputting the intermediate image into a generation module, and generating a clear image after detail reconstruction; the generation module introduces a signal refining module and a frequency modulator for improving the image quality. According to the method, a two-stage model is provided, so that the restoration effect of the composite degraded image is effectively improved.