121 results about "Blind deconvolution" patented technology

A signal processing algorithm has been developed in which a filter function is extracted from degraded data through mathematical operations. The filter function can then be used to restore much of the degraded content of the data through use of any deconvolution algorithm. This process can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated Self-deconvolving Data Reconstruction Algorithm (SeDDaRA), has been used successfully to restore digitized photographs, digitized acoustic waveforms, and other forms of data. The process is non-iterative, computationally efficient, and requires little user input. Implementation is straight-forward, allowing inclusion into all types of signal processing software and hardware.

The novelty of the invention is the application of a power law and smoothing function to the degraded data in frequency space. Two methods for determining the value of the power law are discussed. The first method is by educated guess where the value is deemed a constant of frequency that ranges between zero and one. This approach requires no knowledge of the original data or the degradation and is quite effective. The second method compares the frequency spectrum of the degraded data to the spectrum of a signal with the desired frequency response. This approach produces a superior result, but requires additional processing.

The invention discloses an image de-blurring method based on bright stripe information in an image. Bright stripes existing in a motion blurred image are applied, information of the shape of a blurred kernel is obtained, image restoration is restrained through combining the image and the blurred kernel, and an accurate blurred kernel and a high-quality restoration image are obtained. The method particularly comprises steps: the optimal image block containing the optimal bright stripes is selected; the blurred kernel shape information is obtained through extraction; blurred kernel estimation is carried out to obtain the final blurred kernel; and non-blind deconvolution is carried out, and a distinct restoration image is obtained through restoration to serve as the final de-blurring image. An actually-photographed bright stripe-containing blurred image testing set is built; and by adopting the technical scheme of the invention, the accurate blurred kernel and the high-quality restoration image can be obtained, and the application value in the image processing field is high.

The present invention includes a method and apparatus for the detection and analysis of information-containing signals in chromatographic data using iterative blind deconvolution and fuzzy logic algorithms. The invented method analyzes chromatographic data from a wide variety of sources of DNA sequencing information, including gel and capillary electrophoresis. Autoradiograms, single-fluor, four-lane and four fluor, single lane fluorescent chromatographic data are suitable sources of unprocessed input data. The output from the invented base calling method includes called (identified) sequence data and quality values for the called bases.

The present invention relates to the image achieved by any conventional method of capture and their subsequent computer processing. The process of the invention presented is based on the simultaneous processing of the input images and their restoration (deconvolution) pixel by pixel by means of a new mathematical method. Furthermore, and in the case of having more than one image of the scene in question, the system is capable of providing superresolution of the input images. It has direct application in digital photographic cameras, digital video cameras, mobile phones, video and photography edition programmes, image analysis programmes for microscopy and astronomy, analysis of medical images, forensic image, image-based security systems, aerial image and in the restoration of works of art, among others.

The invention discloses a Bayes' rule based multi-frame blind convolution super-resolution reconstruction method and device. The method includes steps of acquiring an interest area of a reference image and a matching area of a target image subjected to radiation correction through an image quality evaluation and frame selection algorithm for an image sequence of one scene; acquiring the radiancy and an accurate geometric distortion parameter through an image registration algorithm executed on the matching area of the target area subjected to radiation correction; acquiring a point diffusion function of image super-resolution restoration through execution of a multi-frame blind deconvolution image restoration algorithm on the accurate geometric distortion parameter; acquiring a super-resolution reconstruction image through execution of a maximum posterior super-resolution reconstruction algorithm on the radiancy and the point diffusion function of image super-resolution restoration. According to the invention, problems of insufficient consideration of point diffusion function, motion blur, image structure information, sparsity and the like of a traditional general algorithm are solved, automatic estimation on the system point diffusion function and multi-frame image registration parameters is performed, and the image resolution is improved.

Embodiments disclose methods, systems, and computer-readable media for base-calling of sequencing data in the presence of systematic or phasic synchrony errors. These techniques may be adapted to rapidly and accurately resolve signal data arising from a variety of different nucleic acid sequencing platforms. In various embodiments, techniques for blind deconvolution dephasing of nucleic acid sequencing data may be adapted to perform raw signal processing, basecalling, and/or sequence determination.

The invention provides a natural image blind motion blur elimination method based on L0 regularization. Gradient information of a natural image and the sparse characteristic of a motion blur core are utilized, and corresponding L0 regular terms are respectively introduced into a solving model of the blur core. In the model solving process, a half-quadratic splitting method is firstly utilized to respectively solve a middle recovered image and the blur core, a pyramid model is utilized in the solving process for carrying out solving layer by layer so as to increase the robustness, only the gradient information of the image is utilized for the estimation of the blur core, and by means of Fourier transform, the solving process is transformed to frequency domain, so that the deconvolution operation carried out directly on a space domain is avoided, and a rapid solving purpose is achieved; in addition, the obtained blur core is utilized, and a final recovered image is obtained by a non-blind deconvolution method based on the total-variable difference. The method provided by the invention has the advantages that the solving speed is high, the robustness is high, and the final recovered image has a good visual effect.

A method for image restoration and reconstruction registers each frame of an observed image sequence to suppress geometric deformation through B-spline based non-rigid registration, producing a registered sequence, then performs a temporal regression process on the registered sequence to produce a near-diffraction-limited image, and performs a single-image blind deconvolution of thenear-diffraction-limited image to deblur it, generating a final output image.

The invention relates to a self-adaptive optical image high resolution restoration method combined with frame selection and blind deconvolution, comprising the following steps: firstly, a short exposure image sequence gn (x, y) is recorded when a self-adaptive optical closed loop is corrected; shannnon entropy of each frame of image in the sequence is calculated; a degraded image gm (x, y) with lower entropy is selected for blind deconvolution image restoration; secondly, an initial value hm (x, y) of a point spread function is generated by utilization of random phase; thirdly, a target f(x, y) is estimated by using the gm(x, y) and the obtained hm (x, y), and an estimated value f(x, y) is obtained after addition of positivity limitation on the target; fourthly, an estimated value hm (x, y) of the point spread function is obtained by using the gm (x, y) and the f(x, y), and an estimated value h(x, y) is obtained after addition of positivity limitation in the same way; fifthly, inspection is made whether an iterated value h(x, y) and an iterated value f(x, y) meet iteration stopping requirements or not; if the iterated values do not meet the iteration stopping requirements, the third step is returned; if the iterated values meet the iteration stopping requirements, circulation is stopped and the f(x, y) and the h(x, y) are outputted. The invention has the advantages of effective improvement of restoration quality, acceleration of convergence rate, capability of well compensating correction capability under hardware limitation of a self-adaptive optical system, and improvement of imaging quality.

The invention relates to an underwater sound signal blind deconvolution method suitable for shallow sea low-frequency conditions, which is technically characterized by comprising the steps of: estimating a horizontal wave number in a normal vibration mode from a horizontal array receiving signal by using a wave number estimation method, wherein the horizontal wave number is propagated in a waveguide; and then constructing one group of weight vectors according to the information of the estimated horizontal wave number in the normal vibration mode, and carrying out mode decomposition processing on the horizontal array receiving signal by using the weight vectors for recovering a source signal waveform. The restrain optimal weight vector in the invention can also be used for further improving deconvolution performance of the horizontal array manual time reversal blind deconvolution method provided by Sabra and the like.

The invention provides an intelligent time-varying blind deconvolution broadband processing method and device. The method comprises: extracting an initial seismic wavelet from an attenuated seismic data; generating a Q value group including a plurality of stratum quality factors Q values randomly; according to the initial seismic wavelet and the Q value group, establishing a time-varying wavelet matrix group; acquiring a plurality of reflection coefficients according to the attenuated seismic data and the time-varying wavelet matrix group; establishing a fitness function; on the basis of the fitness function and the multiple reflection coefficients, acquiring a plurality of fitness values corresponding to the stratum quality factors Q values one by one; according to the multiple fitness values, determining an optimal stratum quality factors Q value. Because the optimal Q value is obtained by intelligent searching instead of manual determination, the work load and error occurrence are reduced, so that an optimal time-varying wavelet is estimated from unsteady data; and an optimal time-varying blind deconvolution result is obtained.

The invention discloses a nonparametric estimation independent component analysis (ICA)-based multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system blind deconvolution method. The method comprises the following steps of: combining nonparametric estimation ICA and MIMO-OFDM; arranging a signal preprocessing module at a transmitting end of the MIMO-OFDM system to perform non-redundant linear precoding on a frequency domain signal mapped to a subcarrier so as to eliminate fuzziness of an independent component analysis algorithm in reconstructed signal sorting and energy; and arranging a nonparametric ICA module at a receiving end of the system to perform channel estimation so as to restore a source signal. By the method, frequency spectrum resource is saved, the amount of calculation is reduced, the operational speed is increased and a time-varying channel can be estimated well.

A method for image de-blurring includes estimating an intermediate image L by marking and constraining an edge region and a smooth region in an input image; estimating a blur kernel k by extracting salient edges from the intermediate image L, wherein the salient edges have scales greater than those of the blur kernel k; and restoring the input image to a clear image by performing non-blind deconvolution on the input image and the estimated blur kernel k. Imposing constraints on the edge region and the smooth region allows the intermediate image to maintain the edge while effectively removing noise and ringing artifacts in the smooth region. The use of the salient edges in the intermediate image L enables more accurate blur kernel estimation. Performing non-blind deconvolution on the input image and the estimated blur kernel k restores the input image to a clear image achieving desired de-blurring effect.

The invention discloses a shallow sea horizontal array passive positioning method and system based on spatial domain deconvolution processing, and the method comprises the steps: receiving a radiationsignal of a target sound source in an underwater sound field through an N-element horizontal uniform linear array, and obtaining a received signal frequency spectrum of each array element through thefrequency spectrum analysis of a sound pressure signal collected by a hydrophone; obtaining an estimated value of a frequency domain Green function through blind deconvolution calculation according to the frequency spectrum of the received signal; carrying out inverse Fourier transform on the estimated value of the frequency domain Green function, extracting target multipath relative incident time from the obtained array element time domain Green function, and then calculating a target multipath incident angle through a deconvolution processing method; calculating to obtain an array invariantaccording to the target multipath incident angle; and calculating the distance of the target sound source according to the array invariant, thereby realizing positioning of the target sound source. According to the method, steady passive positioning under the shallow sea small-aperture horizontal array condition is realized, and higher passive positioning precision is obtained under the conditionthat the calculated amount is not remarkably increased.

Embodiments related to the removal of blur from an image are disclosed. One disclosed embodiment provides a method of performing an iterative non-blind deconvolution of a blurred image to form an updated image. The method comprises downsampling the blurred image to form a blurred image pyramid comprising images of two or more different resolution scales, downsampling a blur kernel to form a blur kernel pyramid comprising kernels of two or more different sizes, and deconvoluting a selected image in the blurred image pyramid according to a Richardson-Lucy deconvolution process in which a bilateral range/spatial filter is employed.

The invention provides a method for multi-channel blind deconvolution on a cascaded neural network. The method comprises the following steps: (1) forming a module neural network by a balanced sub-network and a compressed sub-network; (2) updating a nerve synapse {wlj,p} of the balanced sub-network by using a constant model algorithm; (3) constituting Hebbian and inverse Hebbian learning rules; and (4) after compression, inputting to the balanced sub-network of a next module network. The method disclosed by the invention is an expansion of a method which is novel, simple and individually effective, and can be used for effectively extracting a plurality of source signals online from unknown and staggered mixed signals, namely each module of the cascaded neural network is composed of the balanced sub-network and the compressed sub-network. The method disclosed by the invention can be applicable to any blind equalization algorithm (an extension of signal channel equalization) and further can be applied to a condition that the quantity of the source signals is unknown in advance. The method disclosed by the invention is easy to realize and can be widely applied to aspects of wireless communication, array processing, biomedical signal processing and the like.

The invention discloses a frequency-domain blind deconvolution method for a voice signal, comprising the following steps of: converting a time-domain convolution mixed voice signal to a frequency domain and then performing blind separation; converting and transforming the time-domain convolution mixed voice signal to a frequency-domain linear instantaneous mixture model via windowed Fourier transform according to the short-time stability of the voice signal; after performing pre-processing such as filtering and whitening in the frequency domain, realizing segmented blind separation for the voice signal by adopting a method of the approximate joint diagonalization of correlation matrices under different time delays; and after solving the problem of the fuzziness of the blind separation for the signal, performing segmented recombination for the separated signals in the time domain via inverse Fourier transform. Via the frequency-domain blind deconvolution method disclosed by the invention, a good separation effect is realized for 2*2 real-time recoded mixed voice signal, and the recognition accuracy of the voice signal of a human-computer interaction system in an environment with the speech interference of other people can be efficiently improved.

The invention discloses a noise blurred image blind deconvolution method based on an image significant structure. The noise blurred image blind deconvolution method comprises steps that to-be-deblurred image data is input; the denoising pre-treatment of the input image data is carried out; the significant edge extraction of the image after the denoising pre-treatment is carried out; an image strong edge is reconstructed by carrying out the Shock filtering of the image after the significant edge extraction; the image significant edge used for blurred kernel estimation is calculated by using the image strong edge; an initial blurred kernel is estimated; the rough image restoration is carried out by using the estimated initial blurred kernel; the blurred kernel based on ISD after the rough image restoration is corrected; and the image is restored. The problem of the image deblurring sensitive to noises is solved, and the blurred kernel of the noise blurred image is estimated accurately, and high quality restored image is provided.

The invention relates to an image resampling operation detection method. The method comprises the steps of representing image resampling operation by using a model; carrying out initial blind deconvolution operation on a resampled image to obtain an initial kernel; working out initial kernels with different sizes so as to obtain a group of convolution kernel sets with multiple scales and different sizes; comparing a result, obtained by carrying out convolution on one convolution kernel in the convolution kernel sets and an original image corresponding to the convolution kernel, with the resampled image, wherein the convolution kernel with the minimum mass difference is an optimal kernel, namely, system output; unfolding the optimal kernel to obtain a column vector; putting the obtained column vector into in a classifier for training a model to obtain a classification model; repeating the step 2 to the step 5 for the image to be detected, and putting the obtained column vector into the classification model obtained in the step 6 for testing so as to obtain a final detection result. The image resampling operation detection method can effectively reduce the training time of the support vector machine (SVM) classifier. Furthermore, the method in high in compression resistance, and has excellent detection accuracy rate.

The invention discloses a multilayer Bayes blind deconvolution method for an SAR image based on a frequency domain and spectrum matrix, and the method comprises the steps: inputting and observing the SAR image g, and giving an observation model; initializing an original SAR image f and a point spread function h as f<0> and h<0>, and giving a prior model; initializing hyper-parameters of the model, setting a confidence value, and giving a prior model; carrying out the zero extending and cyclic shift of a mask, h and h<0> of the prior model as c<es>, h<es>, and h<0><es>, and carrying out the conversion of c<es>, h<es>, and h<0><es> and the image into the frequency domain; constructing and initializing a spectrum matrix through the spectrums of cycle covariance matrixes of f and h<es>; optimizing random distribution to iterate and estimate hyper-parameters, frequency domain h<es> and frequency domain f; converting the frequency domain to a spatial domain, carrying out shifting and zero removing, and outputting the final result of blind deconvolution. The method saves a process of vectoring and matrixing, so as to avoid high-cost superlarge matrix operation. The method employs the frequency domain to represent the vectors and matrixes, employs the spectrums of the matrixes to construct the spectrum matrix, achieves the deconvolution at low operation cost, and effectively improves the operation efficiency of blind deconvolution of the SAR image.

The invention belongs to the radar image processing technology field and discloses an iterative heavy weighted blind deconvolution method of a passive millimeter wave radar image. The method comprises the following steps of (1) integrating an adaptive weight constructed based on a residual error into a data item; (2) integrating a spatial adaptive weight of a coefficient of an image under a bilateral total variational operator effect into an image regularization item; (3) applying a smoothness constraint under a Laplace operator effect on a passive millimeter wave imaging system point expansion function; (4) realizing image and point expansion function combination iteration blind deconvolution under a multi-scale coarse-to-fine framework; and (5) deriving an adaptive estimation formula of a model regularization parameter. In the prior art, during passive millimeter wave image blind deconvolution, a radar is sensitive to a loud noise and a mixing noise and can not recover image edge and detail information. By using the heavy weighted blind deconvolution method of the invention, the above problems are solved.

The invention discloses a high-energy X-ray image blind restoration method and system. The method comprises the following steps: firstly, proposing definition of an image region extreme value according to the characteristic that high-energy X-ray image gray level distribution is concentrated and continuous; then, constraining the regional extreme value and the blurring kernel of the image by using an l0 norm, and constructing an image blind restoration model on an MAP framework in combination with the gradient prior of the image; thirdly, alternately solving a clear image and a fuzzy kernel through a semi-quadratic splitting method and fast Fourier transform, and accelerating the solving of sub-problems by using linear approximation and an acceleration conjugate gradient method; and finally, obtaining the main structure information of the preliminarily estimated blurring kernel through a skeleton extraction and traversal method, constructing a continuous function in a blurring kernel cross sliding window for optimization, and performing non-blind deconvolution on the blurring image by using the optimized blurring kernel k, to obtain a clear image. According to the method, the system blurring in the high-energy X-ray image is better removed, the quality of the image is improved, and a more accurate blurring kernel is estimated for nonlinear reconstruction of the image.