497 results about "Deblurring" patented technology

Systems and methods for providing a substantially de-blurred image of a scene from a motion blurred image of the scene are disclosed. An exemplary system includes a primary detector for sensing the motion blurred image and generating primary image information representing the blurred image, a secondary detector for sensing two or more secondary images of the scene and for generating secondary image information representing the two or more secondary images, and a processor for determining motion information from the secondary image information, estimating a point spread function for the motion blurred image from the motion information, and applying the estimated point spread function to the primary image information to generate information representing the substantially de-blurred image.

Input data comprising a video signal is processed using a combination of a known image processing method to deblur, or sharpen, the image and convolution with Pixon™ kernels for smoothing. The smoothing process utilizes a plurality of different size Pixon™ kernels which operate in parallel so that the input data are convolved with each different Pixon™ kernel simultaneously. The smoothed image is convolved with the point response function (PRF) to form data models that are compared against the input data, then the broadest Pixon™ kernel that fits the input data within a predetermined criterion are selected to form a Pixon™ map. The data are smoothed and assembled according to the Pixon™ map, then are deconvolved and output to a video display or other appropriate device, providing a clearer image with less noise.

The present Invention relates to a camera system suitable for use in minimally invasive surgery (MIS), among other applications. In at least one embodiment, the camera system includes an autonomous miniature camera, a light source assembly providing features such as steerable illumination and a variable radiation angle, and a control and processing unit for processing images acquired by the camera Io generate improved images having reduced blurring using a deblurring algorithm.

A method for prediction of an indication of motion using input from an event-based camera includes receiving events captured by an event-based camera, wherein each of the events represents a location of a change in pixel intensity, a polarity of the change, and a time. The method further includes discretizing the events into time discretized event volumes, each of which contain events that occur within a specified time range. The method further includes providing the time discretized event volumes as input to an encoder-decoder neural network trained to predict an indication of motion using a loss function that measures quality of image deblurring; generating, using the neural network, a prediction of the indication of motion. The method further includes using the prediction of the indication of motion in a machine vision application.

The invention belongs to the technical field of computer digital image processing, and particularly relates to an image deblurring method based on an aggregation expansion convolutional network. The method comprises the steps of constructing a deep neural network, generating a network based on a condition countermeasure, wherein the network comprises a generator and a discriminator, the generatorstructure uses a stacked self-encoder module, and the self-encoder module is connected with a jump through a self-encoder structure, a residual error module is used on a construction module, residualerror module uses a residual network and multi-channel aggregation expansion convolution, and the discriminator uses a five-layer convolutional neural network; training the deep neural network: usingfuzzy image data set in public and real scenes, using image content loss function and a countermeasure loss function to train the deep neural network constructed in the previous step, and using a trained network model to carry out deblurring processing on a blurred image. According to the method disclosed by the invention, the deblurring effect can be ensured, a blurred image can be quickly and efficiently restored to a clear image, and the image deblurring efficiency can be greatly improved.

A method and device for deblurring an image having pixels. A blurred image is downloaded into a systolic array processor having an array of processing logic blocks such that each pixel arrives in a respective processing logic block of one pixel or small groups of pixels. Data is sequentially exchanged between processing logic blocks by interconnecting each processing logic block with a predefined number of adjacent processing logic blocks, followed by uploading the deblurred image. The processing logic blocks provide an iterative update of the blurred image by (i) providing feedback of the blurred image prediction error using the deblurred image and (ii) providing feedback of the past deblurred image estimate. The iterative update is implemented in the processing logic blocks by u(n+1)=u(n)−K*(H*u(n)−y_b)−S*u(n).

The invention discloses a single-image blind motion blur removing method based on a multi-scale residual generative adversarial network. The method comprises the following steps: acquiring a GoPRo paired data set, and connecting the GoPRo paired data set to form an image pair in a fuzzy-clear form; randomly cutting the training image into an image patch with the size of 256 * 256; taking the standardized image as model training input data; designing a convolutional neural network, and outputting a deblurred image; calculating the peak signal-to-noise ratio and structural similarity of the output information of the model and the clear image of the corresponding label, and performing loss optimization; and deblurring a picture with a motion blur scene in reality by using the optimized modelparameters to obtain a corresponding clear picture. Based on the convolutional neural network, the conditional generative adversarial network is adopted as a backbone network, and the fine-grained residual module is adopted as a main body module, so that the breakthrough of converting an image deblurring problem into an image translation problem and solving the image deblurring problem is realized, and important technical support is provided for subsequent operation of image deblurring.

The invention provides an image deblurring method based on camera motion detection. According to the image deblurring method based on the camera motion detection, motion parameters of a camera during exposure are firstly obtained through an inertial navigation device, and a rough movement locus of the camera is estimated according to the motion parameters; then a blurring core of an image during the exposure is obtained through the multi-look geometry according to the movement locus; finally, a sharp image which is deblurred is obtained according to the Wiener filtering algorithm. According to the image deblurring method based on the camera motion detection, the moving condition of the camera during the exposure can be accurately detected, the blurring core can be generated fast and accurately according to the movement locus, and finally the relatively sharp image can be obtained though inverse filtering.

The invention provides an attention mechanism-based image blind deblurring method and system, and the method comprises the steps that a multi-scale attention network directly recovers a clear image inan end-to-end mode, wherein the multi-scale attention network adopts an asymmetric encoding and decoding structure, and the encoding side of the encoding and decoding structure adopts a residual dense network block to complete feature extraction and expression of an input image by the multi-scale attention network; a plurality of attention modules are arranged on the decoding side of the encodingand decoding structure, the attention modules output preliminary restored images, and the preliminary restored images form a pyramid-type multi-scale structure of the image; the attention module further outputs an attention feature map, and the attention feature map models the relationship between the long-distance areas from the global perspective to process the blurred image; dark channel priorloss and multi-scale content loss form a loss function, and the loss function is used for reversely optimizing the network and is not self-optimized.

The invention provides a method and a device for monitoring the abnormal behavior of a motor vehicle on an expressway. The method comprises the following steps of dividing and setting a monitoring area; decoding two paths of split-screen video image streams of a holographic high-definition video cameras into a single-frame image; performing deblurring and background refreshing processing on the image; detecting the motor vehicle, and tracking the motor vehicle; analyzing the driving behavior of the motor vehicle in the image; rotating a high-definition dome camera to the position of the motor vehicle to capture an image; identifying the license plate number of the motor vehicle in the image, combining the identified license plate number of the motor vehicle and the abnormal behavior of the motor vehicle into a warning statement for field playing, and simultaneously giving an alarm. According to the method and the device, the abnormal behavior of the motor vehicle on various rapid expressways is automatically identified for accurate detection and capturing of images, data and the like, and the warning statement is wirelessly transmitted and broadcast through a loudspeaker on the spot to timely prompt a driver to normally drive the motor vehicle, so that the occurrence of a fatal traffic accident can be effectively avoided, and the life and property safety of people is protected.

The invention discloses an image blind deblurring method based on edge self-adaption. To solve the problems that as for an existing total variation deblurring algorithm, edges and details of images are easily blurred, a de-mean gradient total variation canonical model is built, weighting coefficients are calculated in an iterated mode by means of local variance self-adaption of gradients of the images, and the ability of the deblurring algorithm to restore the edges and the details of the images. The image blind deblurring method comprises the following steps that (1) a blurred image is input, solutions to a gradient-region clear image and a blurring kernel are obtained alternately, and the initial blurring kernel of the blurred image is obtained; (2) the initial blurring kernel is used for conducting primary non-blind deblurring on the blurred image, and an initial clear image is obtained; (3) clustering is conducted on the initial clear image, the mean value and the weighting coefficient in the de-mean canonical model are updated, and a solution to the blurring kernel is obtained again; (4) the new blurring kernel is used for conducting secondary non-blind deblurring so as to obtain a clear image. Experimental results show that the image blind deblurring method based on edge self-adaption has better deburring effect than the prior art and can be used for image restoration.

Methods are described for efficient reconstruction of MRI data. In one practice, new reconstruction algorithms for non-uniformly sampled k-space data are presented. In the disclosed algorithms, Iterative Next-Neighbor re-Gridding (INNG) and Block INNG (BINNG), iterative procedures are performed using larger rescaled matrices than the target grid matrix In BINNG algorithm, the sampled k-space region is partitioned into several blocks and the INNG algorithm is applied to each block. In another practice, a novel partial spiral reconstruction (PFSR) uses an estimated phase map from a low-resolution image reconstructed from the central k-space data and performs iterations, similar to the iterative procedures with INNG, with an imposed phase constraint. According to yet another practice, an off-resonance correction is performed on matrices that are smaller than the full image matrix. All these methods reduce the computational costs while rendering high-quality reconstructed images.

A method of using an image capture device to identify range information for objects in a scene includes providing an image capture device having an image sensor, a coded aperture having circular symmetry, and a lens; storing in a memory a set of blur parameters derived from range calibration data; capturing images of the scene having a plurality of objects; producing a set of reference edge images using the blur parameters from the stored set; providing a set of deblurred images using the captured image, the reference edges and each of the blur parameters from the stored set; and using the set of deblurred images to determine the range information for the objects in the scene.

The invention provides a video content replacement method and system based on an adversarial generative network. The method comprises the following steps: extracting a source target image in a video frame; performing semantic segmentation on the source target image; performing data enhancement on the source target image by using the image transformation operation; inputting the semantic segmentation images paired by the enhanced data set into a network training generative adversarial model; accurately detecting an ROI region of the source target image and carrying out cutting and straightening; taking the ROI image as a generation model input to obtain a generated target image and a synthetic mask; controlling edge smoothing and deblurring of the generated target image by using mask control after Gaussian blur; adjusting the hue of the generated target image to be consistent with that of the frame source target image by a histogram matching strategy; generating video frame anti-jitterprocessing; and fusing the source image and generating a target image. Compared with the prior art, the method and system has the characteristics and advantages of easiness in operation, low cost, good effect, short consumed time and the like.

Appropriate deblurring is carried out on a partially blurry image. Edge detection means detects edges in 8 directions in a reduced image, and block division means divides the reduced image into 16 blocks. Analysis means analyzes images in the blocks based on edge characteristic quantities thereof, and judges whether each of the block images is a blurry image. The analysis means obtains a width of blur, a degree of shake, and a direction of blur in each of the blurry block images as blur information, and parameter setting means sets parameters for correction based on the blur information. The parameter setting means also sets a correction strength in such a manner that the strength becomes larger as the width of blur becomes larger, according to the width of blur in the blur information.

The invention discloses a multi-scale non-local regularization blurring kernel estimation method. In image based blind de-blurring problems, posterior sharp images and blurring kernels are unknown, which is a challenging difficulty in image blind de-blurring. According to the method, on the basis of image block posteriori, a multi-scale non-local regularization strategy is introduced. The strategy is added on the two most rough layers of image pyramid calculation blurring kernels, the understanding space range is limited effectively, and accordingly, the blurring kernel calculation is evolved towards the accurate direction, clear blurring kernels are calculated, the basis is laid for the next step of sharp image calculation, image blind de-blurring is achieved through an image non blind de-blurring method, and the experiment result shows that the method is more effective and better than some existing advance methods.

The present invention relates to a method and system for obtaining a point spread function for deblurring image data captured by an imaging device comprising a motion sensor. First, motion path values indicating the motion of the imaging device during the exposure time are acquired. The motion path values of the imaging device are then projected onto the sensor plane and for each sensor pixel the projected motion path values are integrated over time. Said integrated value represents for each sensor pixel an initial estimate of the point spread function. Optionally, the size of the point spread function can also be estimated based on the distance of the focused object and taken into account during the projecting step.

The invention discloses a blind camera shake deblurring method based on the L0 sparse prior, and belongs to the technical field of digital image processing. The blind camera shake deblurring method is a method for deblurring blurred images caused by camera shaking, and various space-unchanged camera shaking blurred kernels, namely the point spread functions, can be estimated. The blind camera shake deblurring method solves the problem that a current variational bayes estimation method is high in computing complexity and solves the problem that a current maximum posteriori estimation method lacks strict optimization theory supports. The blind camera shake deblurring method comprises the steps of firstly, introducing remarkable edge sparse prior based on the L0 norm, and using the iterative hard threshold compressed method to achieve recessive automatic prediction of remarkable edge characteristics, secondly, introducing camera shake blurred kernel sparse prior, and using the iterative repeated weighted least square method to achieve rapid estimation of the blurred kernels, and finally, using the image non-blind deblurring method based on super-Laplacian prior to obtain a high-quality deblurred image. The flow diagram of the blind camera shake deblurring method is shown in the figure 1.

The invention discloses a simultaneous positioning and mapping method for an autonomous mobile platform in a rescue scene, which is applied to a search and rescue autonomous mobile platform and can beused for positioning and mapping in extreme environments such as a fire rescue scene, a complex dangerous accident scene and the like. The method is mainly divided into three parts of sensor information processing, pose estimation and pose correction. The method comprises the following steps: firstly, performing operations such as deblurring and feature extraction on omnibearing image informationacquired by a four-eye camera module; secondly, fusing with the measurement information of the inertial navigation sensor and estimating the pose, and finally, carrying out global pose optimization and correction. According to the method, a full-automatic end-to-end defogging method is designed based on the convolutional neural network, a post-processing step is not needed, the application rangeis wide, and the method can be applied to defogging of indoor and natural scenes at the same time. And meanwhile, a relatively stable binocular vision SLAM method based on a feature point method is adopted to fuse IMU sensor data to perform positioning and mapping of the autonomous mobile platform.

The invention discloses a heart magnetic resonance imaging (MRI) image deblurring method based on sparse low rank and dictionary learning. The heart MRI image deblurring method mainly solves the problem that beating of a heart causes the quality reduction of a heart MRI image. The realization process of the heart MRI image deblurring method includes the steps: inputting a heart MRI image; carrying out sparse low rank matrix decomposition on the heart MRI image, and obtaining a sparse part and a low rank part of the image; selecting a sub window from the sparse part of the image; estimating motion blur kernel in the sub window through a method of self-adaption dictionary learning; carrying out deconvolution operation on the heart MRI image through utilization of the estimated motion blur kernel, and obtaining a clear heart MRI image. The heart MRI image deblurring method has the advantages of accurately estimating the motion blur kernel, and the distortion of image deblurring results due to inaccuracy of estimation of the motion blur kernel is avoided.

The invention discloses an image deblurring method based on a multi-task CNN. The image deblurring method comprises the following steps: (1) acquiring a total training set, a test set and preprocessing; (2) performing multi-scale zooming and parameter setting on the image; and (3) deblurring based on a multi-task convolutional neural network, wherein the multi-task convolutional neural network comprises three scales, and each scale is divided into an image deblurring module, an image detail recovery module and a feature fusion module; the image deblurring module comprises an image encoding block E and an image decoding block D, and the image encoding block E extracts image features and encodes the image features, and then a deblurred image is obtained through the image decoding block D; the image detail recovery module only comprises a convolution feature extraction layer with a residual structure, and the size of a network feature map is kept the same as the input size, and only blurred image high-order features are extracted and fused, and high-frequency information is provided for the final image recovery process; and the feature fusion module comprises an image merging module and a convolution layer.

The invention discloses a single-image super-resolution reconstruction algorithm based on an optical flow method and sparse neighbor embedding. The single-image super-resolution reconstruction algorithm comprises the following steps: firstly, training and learning offline dictionaries: learning corresponding characteristics of brightness components of high/low-resolution images in a training image set; extracting oriented gradient histograms and gradient characteristics of image blocks, carrying out gradient characteristic dimension reducing processing and dividing to obtain a plurality of clustering subsets to form trained dictionaries; at a reconstruction phase, carrying out RGB color to YCbCr space conversion on the low-resolution images; amplifying chrominance components as reconstructed chrominance components; carrying out bi-cubic interpolation amplification on the brightness components; extracting image characteristics and sequentially matching with a plurality of neighborhood image blocks; calculating an optical flow velocity vector and weighting and combining the plurality of neighborhood image blocks to obtain a final matched result; deblurring reconstructed images and carrying out back projection iteration processing to obtain brightness components of a final reconstruction result; and converting the reconstructed images from a YCbCr color space to an RGB color space. The single-image super-resolution reconstruction algorithm has the advantages that the matching of the image blocks is relatively accurate and the super-resolution reconstruction is relatively effective.