36results about How to "Remove blur effect" patented technology

The invention relates to an underwater image enhancement method based on dark channel prior algorithm and white balance. The method achieves better image enhancement through the modification of background light, improves the image definition and contrast, and achieves the purpose of image optimization. The employed technical scheme is that the method comprises the following steps: A, background light processing step; B, dark channel prior step: 1, obtaining a fined transmissivity t(x) after image cut and a defogged image through employing the dark channel prior algorithm; 2, exporting a depth map d(x) through the transmissivity t(x); 3, obtaining a needed mask; 4, extracting an AOI (area of interest); C, white balance step. The method is mainly used in an occasion of underwater image enhancement.

A display apparatus and method thereof are provided in order to provide a high quality image. The display apparatus includes a driving unit which drives light sources, and a controller which controls the driving unit. Therefore, it is possible to reduce motion blur and flicker and maximize the gray level difference, so that a user may be provided with a clear image.

The invention relates to the digital image processing field and particularly relates to a white balance and dark primary color adaptive histogram underwater image enhancement method. The method comprises steps that step 1, color correction for an image is carried out through utilizing a dynamic threshold white balance algorithm; step 2, a dark channel map of the image after color correction is solved through utilizing a dark channel model; step 3, a weight factor of block images is calculated; step 4, underwater image processing is carried out through utilizing a CLAHE method; step 5,the weight factor and gray mapping relationship tables are utilized to acquire a final gray mapping relationship table of a block through fusion calculation; and step 6, a bilinear interpolation algorithm is utilized to calculate a gray mapping value corresponding to each pixel in block images one by one, enhanced block images are acquired, interpolation calculation for pixels connected between the adjacent block images is carried out, and images of underwater images after enhancement are acquired. The method has advantages of simple calculation and strong timeliness.

The invention discloses a video frame rate up conversion method based on a sparse redundant representation model. The method comprises the steps that (1) a forward optical flow vector vt-1,t+1(i, j) at any pixel location (i, j) between front and back frames ft-1 and ft+1 is estimated; (2) according to the assumption of movement symmetry, based on the forward optical flow field vt-1,t+1 of the front frame ft-1, bidirectional three-dimensional recursive searching is carried out pixel by pixel, and the optimal optical flow vector vt, t+1(i, j) at the current pixel position (i, j) is calculated (3) an optical flow vector in the optical flow field vt,t+1 is smoothened pixel by pixel to ensure the optical flow consistency in a local area and prevent a false optical flow vector; (4) front and back frames ft-1 and ft+1 and the optical flow field vt,t+1 are used to construct a sparse reconstruction model of the current frame ft; (5) a regularization parameter and a sparse representation dictionary in the sparse reconstruction model are estimated; and (6) a nonlinear optimization method is used to solve the sparse reconstruction model to acquire the current frame estimation. Compared with the prior art, the method provided by the invention can effectively improve the performance of video frame rate up conversion.

The invention describes a method and apparatus for reducing motion-induced blur inherent in portable image readers. The method consists of adding a time delay to the operation of the reader after the trigger has been activated, in order to delay the start of the acquisition cycle. When the trigger or button on a portable imaging reader is pulled or pressed, the motion imparted on the reader can be excessive. Images acquired during this time generally suffer from excessive blurring. Incorporating a short delay between trigger and acquisition, allows the image acquisition to occur at an optimal time when the reader motion is at a minimum. The time delay may be of a fixed duration in the range of 50 ms to 250 ms. In another arrangement, the time delay may determined by the time it takes for the reader motion to decelerate below a predetermined threshold.

The invention relates to a method for eliminating time-varying blurring effect in sea level synthetic aperture radar imaging, which comprises: primarily imaging sea level SAR back waves by using a conventional SAR imaging algorithm to acquire an SAR sea level image with the time-varying blurring effect; estimating an orientation facing wave number epsilon0 and a distance facing wave number eta0 of a wave principal component in the SAR sea level image by using wave spectrum inversion; calculating an orientation facing speed correction quantity 1 / 2 * epsilon0 (epsilon0 + eta0) and a distance facing speed correction quantity 1 / 2 * eta0 (epsilon0 + eta0) of an SAR platform according to the dispersion relations of the sea level waves, and constructing a corrected matched filter according to the orientation facing and distance facing correction quantities; and matching and filtering the sea level back waves by using the corrected matched filter to finish SAR imaging so as to eliminate the time-varying blurring effect. The method utilizes the dispersion relations of the wave vibrating frequency and the wave number to correct the orientation facing matched filter of the SAR so as to eliminate the SAR image blurring effect brought by the random time-varying property of the sea level, improve the definition of the SAR sea image, and acquire and invert the sea information with higher precision from the SAR image.

An image processing apparatus includes: an exposure compensation unit carrying out exposure compensation of a long exposure image and a short exposure image in keeping with an exposure ratio; a blur information measuring unit using the exposure-compensated images to calculate blur information showing a degree of discrepancy between pixel values at a corresponding pixel position in the long exposure image and the short exposure image; a blending coefficient calculating unit using the blur information to decide a blending coefficient to be used in a blending process on pixel values at the corresponding pixel position in the long exposure image and the short exposure image; and a blending unit carrying out the blending process on the pixel values at the corresponding pixel position in the long exposure image and the short exposure image using the calculated blending coefficient to decide the pixel value of an output image.

The invention provides an image interpolation reconstruct method based on single image with low resolution ratio that is a horizontal collect diffusion technique through using edge information limitation after interpolation using double three interpolations method with present standard. The method can improve a sawtooth effect in the image with high resolution ratio obtained by present standard interpolation method, and ensure detail information in the image from being destroyed. A double-edge filter technique through using edge information limitation can reduce blurring effect in the image with high resolution ratio effectively, and visual effect of the image with high resolution ratio is improved greatly.

The invention discloses a video high-temporal-spatial-resolution signal processing method combining an optical flow method and a deep network. The video high-temporal-spatial-resolution signal processing method adopts a signal and information processing algorithm-based mode to recover and reconstruct a high-spatial-resolution and high-temporal-resolution video sequence, i.e., a video super-resolution reconstruction method. The video high-temporal-spatial-resolution signal processing method comprises the following steps: taking frame sequences of videos in sequence; starting from the third frame of the video, performing optical flow method motion estimation on each frame and front and back frames of the frame; synthesizing the four generated motion estimation images and the intermediate frame into high-dimensional image blocks of five images; constructing an OF deep convolution super-resolution network, extracting image information by a shallow network, and reconstructing a super-resolution image by a last sub-pixel convolution layer; sending the high-dimensional image blocks into a deep convolutional network for training; and finally, sending the degraded video frame or the video frame with relatively low resolution into a network for reconstruction. According to the video high-temporal-spatial-resolution signal processing method, the reconstruction quality is good, and the reconstruction speed is high, and the reconstruction effect is good compared with a traditional video super-resolution model, and real-time video reconstruction can be carried out.

The invention discloses an anti-fuzzy image amplification method which comprises the steps of acquiring an evaluating index for an image fuzzy degree according to the gradient of an input image, calculating an image sharpening intensity parameter according to an amplification factor and an image resolution, and performing sharpening which corresponds with the quality grade of an input image through control of the parameter; and furthermore decomposing an overall amplifying process of the image into a plurality of small-factor step-by-step amplifications, and amplifying the width and the height of the image by 1.2 times according to a common interpolation method until the a total amplification factor requirement is satisfied. The anti-fuzzy image amplification method has advantages of effectively eliminating an inherent fuzzy effect of an interpolation method and remarkably improving definition of an amplified image under a precondition that computing complexity is not remarkably increased.

Data from a target system (616) that exhibits a non-linear relationship between system properties and measurement data, is processed by applying a linear filter (F) to improve resolution and accuracy. The filter reduces inaccuracies that are introduced by an algorithm used to reconstruct the data. The filter is defined by assigning time varying functions, such as sinusoids (200), to elements (210) in a model (Y) of the system to perturbate the elements. The resulting data output from the model is reconstructed using the same algorithm used to subsequently reconstruct the data from the target system. The filter is defined as a matrix (F) that transforms the reconstructed data of the model (X) back toward the known properties of the model (Y). A library of filters can be pre-calculated for different applications. In an example implementation, the system is tissue that is imaged using optical tomography.

Provided in the invention is a license plate positioning method. The method comprises: a shooting device carries out triggered shooting on a license plate by a trigger; an image shot by the shooting device is transmitted to a license plate positioning system by a network, and the license plate positioning system carries out pretreatment on the image processed by the last step to obtain a license plate area image; the license plate positioning system carries out license plate positioning processing on the license plate area image; and inclination correction processing is carried out on the processed license plate area. According to the invention, the accuracy of license plate positioning and the operation speed of the license plate positioning system are improved.

The invention discloses an airport runway foreign object debris detection radar signal preprocessing method. The airport runway foreign object debris detection radar signal preprocessing method comprises steps that S1, a signal model of an ideal medium frequency signal is established, and the signal model of the measured medium frequency signal is established by introducing a phase error function and a phase error exponential function; S2, a phase error function value is calculated according to the medium frequency signal phase error, and the phase error exponential function is acquired according to the phase error function value; S3, the medium frequency signal after radar transmitting signal non-linear correction is calculated according to the signal model of the measured medium frequency signal and the exponential function value of the phase error function; S4, the matched filtering of the medium frequency signal after the radar transmitting signal non-linear correction and the phase error exponential function value in a range frequency domain is carried out; S5, the medium frequency signal after frequency deviation correction is calculated according to the medium frequency signal after the matched filtering and the phase error exponential function value. The frequency deviation existing in linear frequency modulation signals is corrected.

Gamma-ray tracking methods for use with granular, position sensitive detectors identify the sequence of the interactions taking place in the detector and, hence, the position of the first interaction. The improved position resolution in finding the first interaction in the detection system determines a better definition of the direction of the gamma-ray photon, and hence, a superior source image resolution. A PET system using such a method will have increased efficiency and position resolution.

The invention relates to an image denoising method. The method comprises the following steps: 1, acquiring a digital image; 2, analyzing the noise type in the digital image, namely establishing an image histogram of the digital image, respectively adding different types of noise into a noiseless image histogram to form an image histogram model respectively containing Gaussian noise, Poisson noise, speckle noise and salt and pepper noise, and calculating the noise type of the digital image; analyzing a noise type existing in the digital image by using a grey correlation analysis method; 3, after the noise type in the image is analyzed, respectively processing the noise of different types by adopting a corresponding denoising method.

The invention relates to an unmanned aerial vehicle remote sensing technology-based surface vegetation identification method and system, and a readable storage medium. The method comprises the steps of establishing an observation point location, generating an acquisition mode, generating an unmanned aerial vehicle formation according to the acquisition mode, and obtaining formation information; generating scheduling information according to the formation information, and performing position management and control on the unmanned aerial vehicle according to the scheduling information to obtainunmanned aerial vehicle position information; collecting a multi-angle vegetation remote sensing image according to the unmanned aerial vehicle position information, receiving an electromagnetic wavereflection signal, and establishing a vegetation region space through spectral reflectivity characteristics; extracting vegetation space characteristic values, obtaining classification rules, and performing classification processing on the space characteristic values through the classification rules to obtain vegetation information; carrying out geometric decomposition and remote sensing interpretation on the vegetation information to obtain a vegetation type and result information; judging whether a difference value between the result information and preset information is greater than a preset threshold value; and if yes, generating correction information, collecting the position information of the unmanned aerial vehicle through the correction information, and transmitting a correction result to the terminal.

The invention discloses a fuzzy processing method and device for shooting. The device comprises an acquisition module and a processing module, wherein the acquisition module is used for acquiring a visual focus area of user eyeballs for a shot scene after a mobile terminal starts shooting; the processing module is used for performing fuzzy processing on the visual focus area on the basis of retina information of the user. According to the fuzzy processing method and device, fuzzy processing can be performed on the detected visual focus area according to requirements of the user, and the flexibility for fuzzy processing of details and local parts of a shot photo is improved; besides, the fuzzy processing effect on the shot photo can be removed on the basis of requirements of the user, and real scene of the photo can be restored.

The invention belongs to the technical field of image recognition and discloses an image-based parasite egg shape identification system and method. The image-based parasite egg shape recognition system comprises a sampling module, an image acquisition module, an illumination compensation module, an image preprocessing module, a central processing and control module, an image segmentation module, afeature extraction module, a feature recognition module, a judgment and calibration module, an image database construction module and a display module. The method is advantaged in that egg specimenscan be automatically collected through the sampling module, automatic acquisition of images is realized through the image acquisition module, image noise can be effectively removed by performing median filtering on the acquired image, the fuzzy effect on the original image is low while noise reduction is carried out, more details can be reserved, performance of dividing the attribute into a certain data set is measured by obtaining the information gain of each attribute of the decision tree, the current data set is divided, recognition efficiency is improved, and recognition accuracy is guaranteed.

The invention belongs to the technical field of image processing, and discloses a single-frame image super-resolution reconstruction method based on Euclidean subspace group double mapping. The methodcomprises the following steps: firstly, generating an initial sample training set by utilizing natural images, and performing simplification and classification on the initial sample training set to obtain a screened sample training set; secondly, extracting features from the screened sample training set, generating a feature training set, and clustering the feature training set to form an Euclidean subspace group; sequentially calculating a first remapping coefficient from the low-resolution image block to the high-resolution image block in each local subspace of the Euclidean subspace groupby using a linear regression model; applying the first remapping coefficient to each low-resolution image block in the Euclidean subspace group to reconstruct a to-be-selected high-resolution image block, and calculating a second remapping coefficient from the to-be-selected high-resolution image block to the real high-resolution image block; and finally, generating a test sample set by the test image, and outputting a high-resolution reconstructed image by applying the double mapping coefficients. The algorithm software and hardware of the method are low in cost; independent and mobile imageprocessing platforms are also applicable.

Embodiments are disclosed for generating lens blur effects. The disclosed systems and methods comprise receiving a request to apply a lens blur effect to an image, the request identifying an input image and a first disparity map, generating a plurality of disparity maps and a plurality of distance maps based on the first disparity map, splatting influences of pixels of the input image using a plurality of reshaped kernel gradients, gathering aggregations of the splatted influences, and determining a lens blur for a first pixel of the input image in an output image based on the gathered aggregations of the splatted influences.

Embodiments of the invention disclose a method for processing an image during shooting and a mobile terminal. The method comprises the steps of obtaining feature information of a reference image and a target image in acquired images; matching the feature information of the reference image with that of the target image to obtain a matching result; according to the matching result, determining a moving parameter; and according to the moving parameter, moving the target image by a preset moving rule according to the moving parameter so as to enable position information of the target image to be matched with that of the reference image; and storing the moved target image. By implementing the embodiments of the invention, the mobile terminal moves the target image according to the moving parameter for enabling the position information of the reference image to be matched with that of the target image so as to reduce the influence of image blur caused by vibration. Therefore, the display effect of a shot video or photo can be optimized by implementing the method.

The embodiment of the invention discloses a vulgar action recognition method which comprises the following steps: S1, judging whether a video needing to be detected is a short video or not, if the judgment result is yes, performing the next step, and if the judgment result is no, automatically and equally cutting the video needing to be detected into a plurality of segments, and performing the next step; s2, extracting a preset frame number of images from each segment of the plurality of segments to obtain an image frame sequence; s3, substituting the image frame sequence into a training model for processing, and then outputting apparent time sequence information; s4, processing the apparent time sequence information and then outputting a feature vector; and S5, comparing the feature vector with a preset vector threshold, and judging whether the video is a vulgar video or not, thereby realizing timely, efficient, time-saving and labor-saving monitoring of the video, live broadcast and other contents.