44results about How to "Preserve image detail" patented technology

The invention discloses video noise reduction device and a video noise reduction method. The method comprises the following steps of: obtaining a brightness difference histogram of a current image by using a denoising result of a previous frame of image and a gradient magnitude histogram of the current image; carrying out noise level evaluation on the current image according to the brightness difference histogram; calculating the spatial distance of any two pixel points in the current image, so as to obtain the spatial similarity of the any two pixel points; carrying out denoising on the current image according to the spatial similarity; calculating a pixel time domain distance between any pixel point in the current image and the pixel point at the position corresponding to the previous frame of denoised image, and calculating the corresponding time domain similarity; carrying out three-dimensional recursive denoising on the video image according to the obtained time domain similarity, the spatial similarity denoising result and the previous frame of denoising result. By adopting the device and method disclosed by the invention, three-dimensional recursive denoising is carried out by using the pertinence of the pixel in space and time, so that strong complicated noise can be removed; an image detail can be kept; the stability of the denoising effect also can be ensured.

The invention discloses a weighted adaptive super-resolution reconstructing method for an image sequence, which is superior to the conventional method in the aspects of robustness and practicability, and has important application value for obtaining a high-quality image. The method comprises the following steps: (1) acquiring a plurality of continuous frames of low-resolution images obtained by the same sensor, and resampling a low-resolution image sequence to obtain a resampled low-resolution image sequence; and (2) reconstructing a frame of high-resolution image by utilizing the resampled low-resolution image sequence, wherein the method for reconstructing the frame of high-resolution image comprises the following steps: firstly, establishing a high-resolution image degradation model; secondly, converting a solving process of the high-resolution image in the degradation model into an optimizing process of a solution of a reconstruction optimization model of the high-resolution image according to a predetermined degradation model of the high-resolution image and a regularization theory; and finally, optimizing the reconstruction optimization model of the high-resolution image by utilizing a progressive non-convex algorithm so as to obtain an optimal estimation value of the high-resolution image.

The invention discloses an SAR image noise suppression method based on a direction wave domain mixture Gaussian model, belonging to the field of image processing, mainly solving the problems of fuzzydistortion of an SAR image caused by the current denoising method and improvement of excessive zero values added by coset decomposition of direction wave, and comprising the following steps: (1) carrying out cartoon texture model decomposition on the SAR image; (2) carrying out improved direction wave transformation on the texture part; (3) carrying out fitting of the mixture Gaussian model on thehigh frequency subband coefficient of the coset; (4) estimating noise variance; (5) estimating noise-free coefficient by Bayesian Minimum Mean-Square Error (MMSE); (6) carrying out improved directionwave inverse transformation on the noise-free coefficient to obtain the denoised texture part; and (7) adding the cartoon part and the denoised texture part to obtain a denoised SAR image. The SAR image noise suppression method has the advantages of improved definition, better maintenance of detail information and smoother uniform region of the denoised SAR image and can be used in filtering speckle noise in the SAR image.

An embodiment of the invention provides an image processing method. The method comprises the steps of obtaining a to-be-processed image; determining a definitional domain weight coefficient; determining a value domain weight coefficient according to pixel value differences between a neighborhood of a central pixel point and neighborhoods of surrounding pixel points of the central pixel point, wherein the central pixel point is any pixel point of the to-be-processed image; determining a target weight coefficient according to the definitional domain weight coefficient and the value domain weight coefficient; and performing bilateral filtering on the to-be-processed image by adopting the target weight coefficient. An embodiment of the invention furthermore provides a terminal. Through the method and the terminal provided by the embodiments of the invention, the facial beautification effect can be more natural on the basis of keeping image details.

The invention discloses a space-time united image sequence multi-scale geometric transformation denoising method. By that an image multi-scale geometric analysis is introduced into the image denoising process, and image sequence space-time relevant information is united, a statistical distribution model of an image multi-scale geometric transformation domain coefficient, the built image statistical model serves as priori knowledge, a Bayesian lest square estimation method is adopted to obtain an optimal estimation result of non-noise-pollution image signals, and the problems that the situation that an image detail coefficient and image noise are difficult to distinguish often occurs in the denoising process of a conventional wavelet domain are solved. Meanwhile, plenty of image samples which are the same to a to-be-processed image in scene are collected and image statistical distribution modeling is conducted on the image samples, the statistical distribution model of the image multi-scale geometric transformation domain coefficient directly reflects statistical distribution features of texture details of edges and surface of the to-be-processed image and provides reliable priori distribution knowledge for Bayesian lest square estimation of the image, and space-time information united image sequence non-noise image signal estimation is achieved. A denoising effect of the image is improved, at the same time, image details are kept to a great extent.

The invention discloses an image processing method and apparatus, belonging to the image display field. The method is based on brightness compensation and contrast-tone adjustment, and successively performs brightness compensation and contrast-tone adjustment on an image to be displayed to obtain an output image after compensation processing, allowing the output image displayed by a display device to still possess enough contrast and tone, and meanwhile guarantee image details when the display device reduces brightness, and thereby allowing the image displayed by the display device to possess enough visibility while reducing the power consumption of the display device.

The invention provides a non-uniform illuminance image enhancement method, and belongs to the technical field of digital image processing. The method aims to solve the problems of an image enhancement method in the prior art; according to the method, image enhancement processing can be carried out on a non-uniform illuminance image. The non-uniform illuminance image enhancement method includes the following steps that a histogram of the non-uniform illuminance image is made, a threshold value is determined according to peak values of high illuminance and low illuminance, the histogram is divided into a high illuminance sub-band and a low illuminance sub-band, a sub-band brightness enhancement method is maintained for the high illuminance sub-band and a corresponding gray level mapping enhancement method is adopted for the low illuminance sub-band. According to the non-uniform illuminance image enhancement method, the situation that the image is excessively enhanced locally is avoided, overall noise of the image is inhibited, and the image which is uniform on the whole is obtained. The calculation amount of an algorithm is little, and the method is stable and reliable and can be used in real-time project systems.

The invention provides an iterative noise reduction method based on image low-rank performance. The method concretely relates to the field of image processing and computer vision. In the image representation, Y refers to an image having noise, X refers to an ideal image, N refers to Gaussian white noise of which the standard deviation is tau, and the image is described as Y=X+N and denoising means to reduce the value of N. In order to better utilize the image low-rank performance to perform denoising, firstly an observation image is divided into multiple small blocks which are identical in size. As for any image block P, image blocks similar to the image block P are searched according to a certain similarity criterion. The similar image blocks are vectorized so that a similar block matrix P is formed, based on which the P is estimated by utilizing the low-rank performance of the similar block matrix and the theory of minimum variance estimation so that the objective of suppressing image block noise can be achieved. Finally the denoised image is obtained through reconstruction of the image block estimation value. As for the problem of a small amount of residual noise, the denoised image is processed by adopting a synthetic iterative method.

The invention discloses a convolutional neural network (CNN) model generation method. The method comprises the following steps: constructing a CNN model structure and setting a loss function expression; A plurality of training image pairs are acquired, each training image pair comprises an input image and a target image, and the input image and the target image are images obtained by shooting thesame scene through a mobile terminal and a single lens reflex; Each training image pair is registered, so that an input image included in the training image pair is aligned with the target image; Using the registered training image pair as a training sample to train a CNN model; and continuously updating model parameters of the CNN model, calculating a loss function value of the CNN model according to the loss function expression every time the model parameters are updated, and when the loss function value is converged, stopping the training process to obtain the model parameters of the CNN model so as to generate the CNN model. The invention also discloses a corresponding image quality optimization method and computing equipment.

The invention relates to a glass defect type identification system, which comprising a glass accommodating platform, an image acquisition device, a defective image detection device and an embedded processing device. The glass accommodating platform is used for placing an entire to-be-detected piece of glass. The image acquisition device is used for acquiring the glass images of the entire to-be-detected piece of glass. The defective image detection device is used for determining a defective image out of the glass image. The embedded processing device and the defective image detection device are connected. Based on the defective image, the defect type of the glass can be determined. By adopting the above system, different defect detection parameters are determined for different glass types, so that the identification accuracy of glass defects can be improved.

The invention discloses a collocation method of a transceiving channel of an ultrasonic imaging system; for a transceiving sub-array composed of adjacent array elements, the number of the array elements of the transceiving sub-array is set to be n exponents of 2, and when n is a positive integral larger than or equal to 2, the following steps are conducted: each array element, which is positioned at the symmetrical and middle part of the transceiving sub-array and is not less than 2 and not more than n exponents of 2 minus 2 and is the integral multiple of 2, is correspondingly collocated with a receiving channel. The collocation method of the transceiving channel of the ultrasonic imaging system not only can maintain the detail of the images formed by ultrasonic echo signals and improve image quality, but also can reduce the number of channels, thus guaranteeing the image quality (resolution, penetrating power and intensity) and cost reliability at the same time.

The invention provides a method for image noise reduction processing. The method for the image noise reduction processing comprises the following steps: a directional template is established and weight deviation is calculated according to the directional template; a weight deviation mean value is calculated according to the weight deviation; a determinative factor is calculated according to the weight deviation mean value; a weight filtering mean value is calculated according to the result and output as a strengthened image. According to the technical scheme, noise reduction of the image is achieved in a self-adaptive mode through consideration of directional information and deviation information. Meanwhile, image details can be well kept, the visual effect of the image can be improved in a self-adaptive mode, so that the influence by the noise is reduced to a certain extent and the method for the image noise reduction processing and the system for the image noise reduction processing are used for the noise reduction of video images.

The present invention relates to a glass defect type identification method. The method comprises: 1) providing a glass defect type identification system, wherein the identification system comprises a glass holding platform, an image collecting device, a defect image detection device and an embedded processing device; the glass holding platform is used for placing a to-be-tested glass monoblock; the image collecting device is used for collecting glass images of the to-be-tested glass monoblock; the defect image detection device is used for determining a defect image among the glass images; and the embedded processing device is connected with the defect image detection device, and determines a glass defect type based on the defect image; and 2) using the identification system to perform identification. According to the method provided by the present invention, different defect detection parameters can be determined according to different glass types, so that the accuracy of glass defect type identification is improved.

The invention discloses an image denoising method for adaptive equidistant template iteration mean filtering, aimed at addressing the problems of current adaptive methods, such as poor denoising effect and poor recovery quality. The method includes the following steps: (1) using an extremum method to determine noise spots; (2) conducting sliding rotation with the middle points on 4 edges of a filtering window as start points and 4 angular points as terminal points, taking 8 symmetrical points of the 4 edges of the filtering window to construct an equidistant template (the 4 middle points and 4 angular points on the filtering window are 8 value template special cases), and forming a first equivalence template, a second equivalence template......, and conducting recursion clipping mean filtering with the equivalence templates; (3) checking whether a noise point is completed after the completion of each filtering from 3X3, and if the noise point is not completed, enlarging the filtering window and stops until 7X7, and forming adaptive filtering; (4) if the noise point is not finished, adopting iteration filtering. According to the invention, the method effectively processes noise and at the same time can better protect image details, has a higher rate of using information and has a rapid denoising speed.

The invention relates to an artificial intelligence technology, and discloses a multispectral analysis-based night vision image quality improvement method, which comprises the following steps of: partitioning images in an original night vision image set, and carrying out adaptive training by utilizing the partitioned night vision images to obtain a local sparse structure dictionary; performing sparse denoising on the images in the original night vision image set by using the local sparse structure dictionary to obtain a night vision denoised image set, performing spectral filtering processing and color enhancement processing on the images in the night vision denoised image set to obtain an enhanced image set, and performing color recovery processing on the images in the enhanced image set to obtain an enhanced image set; and obtaining a standard night vision image set, and carrying out sub-histogram equalization processing on the images in the standard night vision image set to obtain an enhanced night vision image set. The invention further provides a night vision image quality improving device and equipment based on multispectral analysis. The method can improve the night vision image quality.

The invention discloses an SAR image segmentation method based on decomposition evolution multi-objective optimization and FCM. The method mainly solves the problem that in the prior art of image segmentation, image segmentation precision is not high, the evaluation index is single, and the segmentation effect is not ideal. The method comprises the steps that the Gabor feature and gray level symbiotic feature of each pixel of an image are extracted, and a superpixel is obtained through rough segmentation of a watershed, superpixel features are used as data to be clustered, a clustering center is used as individual species, the species are optimized through the decomposition evolution multi-objective method, the species obtained after evolution are used as the clustering center to initialize the FCM algorithm, a new clustering center is obtained and used as new species for participating in the next evolution of the decomposition evolution multi-objective algorithm. According to the SAR image segmentation method, the better clustering center is obtained through cross adoption of the decomposition evolution multi-objective algorithm and the FCM algorithm, the defect that the FCM initial value is sensitive and falls into a local optimal solution easily is overcome, and the better image segmentation result can be obtained.

The embodiment of the invention discloses an image processing method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the neighborhood search of a targetpixel point in an image, obtaining a pixel block, determining the noise variance distribution information of the pixel block, and determining the difference between a plurality of adjacent pixel points and the target pixel point through the noise variance distribution information; judging whether each adjacent pixel point in the plurality of adjacent pixel points is similar to the target pixel point or not according to the noise variance distribution information to obtain a plurality of reference pixel points similar to the target pixel point; assigning a weight to each reference pixel pointin the plurality of reference pixel points to obtain a plurality of weights; determining a target pixel value of the target pixel point according to the plurality of weights, the plurality of reference pixel values corresponding to the plurality of reference pixel points and the current pixel value of the target pixel point. Refined image restoration can be realized for the image according to theplurality of reference pixel points, image details are ensured while the noise of the image is reduced, and the overall quality of the image is further improved.

The invention relates to an ultrasonic medical image speckle noise inhibition method based on multi-scale anisotropic diffusion, belonging to the ultrasonic medical image noise inhibition method and aiming to solve the problems that the image edge and the image noise are difficult to distinguish by taking the gradient as the edge detection operator when the existing anisotropic diffusion technology is applied to the ultrasonic image. The ultrasonic medical image speckle noise inhibition method based on multi-scale anisotropic diffusion comprises the following steps: (1) carrying out wavelet decomposition on the image, and calculating standard model values; (2) if the first iteration is carried out, estimating the distribution parameters of the standard model values, and otherwise, executing the step (4); (3) classifying to obtain noise smoothing areas under all scales; (4) determining diffusion thresholds by using the mean value of the standard model values of a noise area, and substituting the diffusion thresholds for diffusion functions to obtain diffusion parameters; (5) weighting wavelet coefficients by using the diffusion parameters, and carrying out inverse wavelet transform; and (6) comparing diffusion thresholds in adjacent two-time iterations, and judging whether to stop. The image is processed under multiple scales, thus the result is more accurate. The ultrasonic medical image speckle noise inhibition method based on multi-scale anisotropic diffusion can be widely applied to various occasions needing to process the ultrasonic images.

The invention discloses video noise reduction device and a video noise reduction method. The method comprises the following steps of: obtaining a brightness difference histogram of a current image by using a denoising result of a previous frame of image and a gradient magnitude histogram of the current image; carrying out noise level evaluation on the current image according to the brightness difference histogram; calculating the spatial distance of any two pixel points in the current image, so as to obtain the spatial similarity of the any two pixel points; carrying out denoising on the current image according to the spatial similarity; calculating a pixel time domain distance between any pixel point in the current image and the pixel point at the position corresponding to the previous frame of denoised image, and calculating the corresponding time domain similarity; carrying out three-dimensional recursive denoising on the video image according to the obtained time domain similarity, the spatial similarity denoising result and the previous frame of denoising result. By adopting the device and method disclosed by the invention, three-dimensional recursive denoising is carried out by using the pertinence of the pixel in space and time, so that strong complicated noise can be removed; an image detail can be kept; the stability of the denoising effect also can be ensured.

The invention discloses a gradient-guided TV-Retinex single-frame image defogging method, belonging to the field of computer vision. The gradient-guided TV-Retinex single-frame image defogging methodcomprises the steps of firstly, realizing brightness offset correction by adopting a half-quadratic punishment calculation method and utilizing a gradient similarity according to a mechanism of imagedegradation caused by fog, and then combining a variational frame (TV-)Retinex and adopting a Split Bregman algorithm to perform image refinement and enhanced defogging. The gradient-guided TV-Retinexsingle-frame image defogging method has the advantages of being capable of restoring a clear image from an original image directly without pre-storing a fog-free image for reference, correcting imagebrightness, improving image detail definition, restoring a clear fog-free image, completing the defogging processing quickly, being adapt to real-time application, and improving the reliability and efficiency of an outdoor intelligent machine vision system.

The invention discloses a Contourlet domain image denoising method based on Treelet, mainly solving the problem of poor denoising effect of the existing denoising method. The Contourlet domain image denoising method comprises the implementation steps of: (1) solving a residual variable matrix by a noise-containing image NI; (2) carrying out non-local mean prefiltering on the NI and obtaining a filtered image LI; (3) carrying out translation on the LI to obtain an image PI after translation, and carrying out Contourlet decomposition on the PI; (4) carrying out denoising respectively on all decomposed high-frequency subbands, carrying out Contourlet inverse transformation on the denoised subbands and obtaining a denoised image FI; (5) carrying out inverse translation on the FI and obtaininga denoised image; and (6) repeating the steps (3)-(5) for eight times to denoise and averaging, and outputting the eight denoised images. The Contourlet domain image denoising method can effectively remove the noise in natural images containing Gaussian white noise, and can be used for change detection and image preprocessing in target identification.

The invention belongs to the technical field of modern art design, and discloses a system and a method for analyzing the representation of Chinese traditional elements in modern art design. The systemfor analyzing the representation of the Chinese traditional elements in the modern art design comprises a design drawing acquisition module, a main control module, an image feature extraction module,a matching module, a classification module, a retrieval module, an analysis module and a display module. Pictures are classified and stored through the classification module according to different numbers of the pictures, so that the classification efficiency of the design drawing is greatly improved; Meanwhile, a Gram matrix is used as an operator for describing image style characteristics through a retrieval module; style characteristics of all training samples of a given design image set are extracted, an image index is constructed, style characteristics of a to-be-detected image are extracted, an index number of the image with the highest similarity with the to-be-detected image is searched, and a similar image set is returned according to the index number, so that retrieval of the abstract image is realized through the method, and the method has the advantages of simplicity and high retrieval efficiency.

The invention provides a method for image noise reduction processing. The method for the image noise reduction processing comprises the following steps: a directional template is established and weight deviation is calculated according to the directional template; a weight deviation mean value is calculated according to the weight deviation; a determinative factor is calculated according to the weight deviation mean value; a weight filtering mean value is calculated according to the result and output as a strengthened image. According to the technical scheme, noise reduction of the image is achieved in a self-adaptive mode through consideration of directional information and deviation information. Meanwhile, image details can be well kept, the visual effect of the image can be improved in a self-adaptive mode, so that the influence by the noise is reduced to a certain extent and the method for the image noise reduction processing and the system for the image noise reduction processing are used for the noise reduction of video images.

The invention discloses a gradient-guided TV-Retinex single-frame image defogging method, which belongs to the field of computer vision. The method first uses gradient similarity according to the mechanism of image degradation caused by fog, and uses a semi-quadratic penalty calculation method to achieve brightness offset correction, and then combines the variational framework (TV‑) Retinex with the Split Bregman algorithm for image refinement enhancement and dehazing. The advantage of this method is that it can directly restore a clear image from the original image without pre-stored fog-free images as a reference, and can correct image brightness to improve image details and clear images to restore clear and fog-free images; it can quickly complete the defogging process and can adapt to real-time applications , Improve the reliability and efficiency of outdoor intelligent machine vision systems.

The invention relates to a time domain and space domain combined infrared image real-time inter-frame noise reduction method and device, and the method comprises the steps of receiving original infrared image data, and carrying out the preprocessing; for the preprocessed infrared image data, utilizing a filtering algorithm based on edge judgment to remove isolated noise points on the edge of the infrared image; suppressing noise on an infrared image spatial domain by using a spatial domain filtering method of retaining edge data; initializing a static image template based on the obtained infrared image data; marking dynamic pixel points and static pixel points of infrared image data by using continuous multi-frame original infrared image data; updating the static image template according to the marked dynamic pixel points and static pixel points; and processing the original infrared image data of the current frame by using the updated static image template. According to the invention, the infrared image can be processed in real time, and the problems of unclear edge and smear of the infrared image are effectively solved.

The embodiment of the invention discloses an image processing method and device, electronic equipment and a medium. The method comprises the following steps: generating a sub-noise value of each pixelin a first frequency domain image according to the power spectrum of the first frequency domain image; determining a power value of each pixel according to the power spectrum; generating a filteringintensity value of each pixel according to the sub-noise value of each pixel and the power value of each pixel; and generating a second frequency domain image according to the filtering intensity value of each pixel. By implementing the embodiment of the invention, refined filtering can be realized for the image, the noise of the image is reduced while the details of the image are ensured, and theoverall quality of the image is further improved.

The invention discloses an image processing method and device, belonging to the field of image display. The invention includes an image processing method based on brightness compensation and contrast-tone adjustment, by sequentially performing brightness compensation and contrast-tone adjustment on the image to be displayed. In the present invention, the output image after compensation processing is obtained by sequentially performing brightness compensation and contrast-tone compensation on the image to be displayed, so that when the display device reduces the brightness, the output image displayed by the display device still has sufficient contrast and tone, and at the same time Guarantee the image details to the greatest extent. Therefore, it is ensured that the images displayed by the display device still have sufficient visibility when the power consumption of the display device is reduced.

The invention discloses dynamic backlight liquid crystal display system and method. The system comprises a pixel brightness computing unit, a brightness histogram counting unit, a brightness representative value computing unit, a time domain filtering unit, a video signal compensating unit and a nonlinear processing unit, wherein the pixel brightness computing unit, the brightness histogram counting unit, the brightness representative value computing unit and the time domain filtering unit are sequentially connected; and the time domain filtering unit is respectively connected with the video signal compensating unit and the nonlinear processing unit. The invention can effectively reduce the energy consumption of a liquid crystal display device, improve dynamic contrast and color saturation of displayed images, effectively improve image quality problems brought by dynamic backlight liquid crystal display, restrain flicker among frames, and keep image details.

The invention discloses a collocation method of a transceiving channel of an ultrasonic imaging system; for a transceiving sub-array composed of adjacent array elements, the number of the array elements of the transceiving sub-array is set to be n exponents of 2, and when n is a positive integral larger than or equal to 2, the following steps are conducted: each array element, which is positionedat the symmetrical and middle part of the transceiving sub-array and is not less than 2 and not more than n exponents of 2 minus 2 and is the integral multiple of 2, is correspondingly collocated with a receiving channel. The collocation method of the transceiving channel of the ultrasonic imaging system not only can maintain the detail of the images formed by ultrasonic echo signals and improve image quality, but also can reduce the number of channels, thus guaranteeing the image quality (resolution, penetrating power and intensity) and cost reliability at the same time.