127results about How to "Preserve image details" patented technology

The invention provides a compressed sensing theory-based reconstruction method of a magnetic resonance random sampled K space data image. The reconstruction method applies a contourlet conversion and iterative soft thresholding method to realize reconstruction of a magnetic resonance image. The method comprises the following steps: collecting K space data in a magnetic resonance image scanner according to a preset observation matrix phi to generate a measurement value, and keeping y; acquiring y from a coil of the magnetic resonance image scanner, and transmitting y to a computer; and finallyconstructing a same phi, constructing any orthogonal transformation psi, and recovering from y by adopting a compressed sensing theory-based magnetic resonance random sampled K space data image reconstruction method according to reconstruction. According to the method, scanning time is saved, quick imaging is realized, high-quality reliable image information is provided to medical nuclear magnetic resonance imaging detection, and solid theoretical and practical foundation is established for further development and large-scale popularization and application of the medical imaging detection technology.

The invention relates to a motion-estimation-based road video stabilization method, which comprises the following steps of: performing nonlinear smooth denoising on an input image; determining a characteristic searching range according to the requirements of a system on accuracy, and acquiring optimal characteristic information; performing cross grayscale projection on a template and a block to be matched, and finding an optimal matched area according to a difference; constructing a dynamic motion model in a recursion way by adopting a Kalman filter with a uniform motion equation, obtaining motion parameters of a camera for describing inter-frame motions caused by the motions of the camera, and identifying and filtering random jitter in an offset sequence obtained by motion estimation; and filling an area losing an image due to the video jitter, and reconstructing adjacent frames by adopting a hybrid Gaussian model and utilizing time relativity between image sequences.

Disclosed is a blind restoration method for the motion blurred image; the steps are as follows: (1) the image is converted through the cepstrum method to figure out the blur extent and the blur direction of the blurred image; (2) the blur extent and the blur direction of the blurred image figured out in step (1), and the total variation (TV) method are adopted to process the restoration towards the blurred image. The method based on the total variation (TV) includes: the fixed-point iteration used as the outer-loop and the conjugate gradient method used as the inner-loop are adopted for loop iteration to obtain the restored image. The blind restoration method has the advantages of strong self-adaptation, strong anti-noise ability and good robustness; the blind restoration method has validity and practicality according to the processing effects of the simulation picture and the photographed picture.

The invention discloses a de-noising method of filtering images in size adaptive block matching transform domains. Less false signals are introduced as two-dimension transformation of each image block in the block matching 3D (BM3D) in a basic estimation stage is abandoned by the method; image details can be well preserved as the block number in blocking matching groups of the method is less than the block number in the BM3D method. The image de-noising performance of the method is further improved as the method adaptively selects the block size based on form components during block matching. The current general objective evaluation of image de-nosing includes peak signal noise ratio (PSNR) and mean structural similarity (MSSIM), and according to the method, the de-noising calculation results of a plurality of standard images provided on BM3D networks are higher than the results of the BM3D method on the basis of the two objective evaluations and under all noise intensities.

According to the legend information, in view of the different characteristics of the color, shape, texture and spatial relationship of various elements on a geological map, techniques of color segmentation, boundary tracking, and mode identification are adopted to extract the main information, e.g. stratum, magmatite, construction, or ore occurrence, related to mineral products in the geological map to form a thematic information graph layer. The geological map thematic information extraction method includes a geological map data preprocessing method, a geological map thematic information extraction method, and an information output method. The method aims to solve the difficulty in geological map information extraction, namely, takes a semi-structured standard geological map as a research object and the geological map information extraction as objective, and extracts the main information, e.g. stratum, magmatite, construction, or ore occurrence, related to the mineral products in the geological map through the combination with legend information to construct the thematic information graph layer, thereby forming a set of techniques for converting the semi-structured data (image) into structure data.

The invention provides an image noise reduction method and device, electronic equipment and a storage medium. The method comprises the following steps: determining exposure parameters according to a shooting scene, shooting by adopting the exposure parameters to obtain a shot image, determining a matched noise reduction model according to the equipment attribute of imaging equipment for shooting, and inputting the exposure parameters and the shot image into the noise reduction model to perform noise reduction on the shot image, wherein the noise reduction model is learned to obtain a mapping relation between the exposure parameters and the noise characteristics. According to the method, after the matched noise reduction model is determined according to the equipment attribute of the imaging equipment used for shooting, noise reduction processing is carried out on the shot image shot in the current shooting scene, and more image details are reserved while the image purity is ensured. Besides, the shot image is subjected to noise reduction through the noise reduction model matched with the equipment attribute, the noise characteristic can be better identified, the purpose of noise reduction is achieved, the noise reduction effect of the image is improved, and a clearer image can be obtained.

The invention provides an image processing method and device based on multiple frames of images. The method comprises the steps that multiple frames of original images are acquired, a neural network model corresponding to each frame of original image is selected according to the exposure degree of each frame of original image for noise reduction processing, so that each frame of original image after noise reduction is obtained, and a target image is synthesized through each frame of original image after noise reduction. Therefore, by performing noise reduction processing on multiple frames oforiginal images, the effectiveness of noise reduction is improved, image details are reserved while image noise is reduced in the synthesized target image, an imaging effect with better definition isobtained, and the imaging effect of the target image is improved.

The invention discloses a magic square coding-based median filter method comprising the following steps of: firstly, reading the coordinates and the gray value of each pixel in an image; secondly, traversing the image and judging whether each pixel is a noise pixel or positioned in a linear detail area or not by using a magic square coding-based method; and thirdly, maintaining the gray value of a non-noise image unchanged and removing the noise of the noise pixel by amending the gray value of the noise pixel. The invention can be used for judging whether the pixel processed as the noise pixel is positioned in the linear detail area or not without generating pixel loss in the linear detail area, thereby ensuring the integrity of image information; the pixels in the linear detail area are judged by using the magic square coding-based method with small calculated quantity; and a proper value is selected to replace the noise pixel according to the difference condition between the gray median value and the mean value of the maximum gray value and the minimum gray value so as to reduce the fuzzy phenomenon caused during the the image noise removing processing.

The invention discloses an average filtering method eliminating image impulse noise fast and efficiently. The method comprises the following steps that filtering windows of images are set in the 8-bit grey-scale images, if the grey value of center pixels is 0 or 255, the pixels are suspected to be the noise pixels, and otherwise the pixels are the signal pixels; if the pixels are suspected to be the noise pixels, the signal pixels are extracted from the boundary pixels to form a set, and if the pixels are the signal pixels, the pixels are not processed; if the set formed by the extracted signal pixels is a non-null set, processing is not carried out, and if the set is a null set, two is added to the number of the filtering windows until the new set is a non-null set; the element grey value average of the set is used for replacement, and processing of the above steps is repeated until all suspected noise pixels obtain the filtered images. The method has good abilities to eliminate noise and maintain details, is high in practicality, and achieves adaptive filtering of the images, high-density impulse noise can be eliminated simply and efficiently, the edge and image details are retained, and the efficiency is higher than that of a traditional filtering method.

The invention discloses a self-adaptive median filtering denoising method applied to image processing, which comprises the following steps of: firstly preprocessing an image, and then, (1) sorting data in a filtering window by adopting a divide-and-conquer sorting strategy; And (2) adaptively adjusting the size of a filtering window by combining the noise density. The adaptive median filtering denoising method applied to image processing is high in denoising efficiency and easy to implement.

The invention discloses an L1 norm total variation regularization heterogeneity correction method based on anisotropy. The method comprises the following steps that: establishing anisotropy total variation regularization model based on a single image, converting a problem of removing infrared image heterogeneity stripe noise into a minimized total variation problem, then, adopting a separated Bragg iteration method to carry out optimization to obtain an optimal solution, and taking a last iteration result as a corrected infrared image. The method has an innovation point that a traditional total variation model aims at a structural feature improvement equation that the horizontal direction total variation of heterogeneity stripe noise is far greater than vertical direction total variation,a regularized constraint based on the L1 norm can be used to enable the method to be suitable for the heterogeneity correction of the infrared image, the separated Bragg iteration method replaces a steepest gradient descent method to carry out the optimal processing of the equation, processing speed is greatly improved, and the edge information of an object is kept to a maximum degree.

The invention discloses a removal method of a zipper effect of bayer interpolation. The bayer interpolation is a device of interpolating missing color components of pixel points of a color filter array; and the zipper effect is an image error appearing most easily in the bayer interpolation. According to the characteristics that the zipper effect mutational in a brightness domain and even in a chroma domain, the method comprises the steps of converting RGB (red, green and blue) image data after the bayer interpolation to a brightness-chroma color space (such as YUV and Lab) via color space transformation, determining texture types of regions where pixel points of an image brightness component are located one by one, performing low-pass filtration in case of an even region, performing horizontal and vertical filtration in case of a horizontal or vertical marginal region, constituting a new image with the filtered image brightness component and the original chroma component, and performing color space inverse transformation to form a final RGB image. The method can effectively eliminate the zipper effect generated by the bayer interpolation and significantly improves image quality.

The embodiments provide an image processing method and apparatus, which combine a wavelet transform method with a non-local means method, comprising: performing N-level decomposition of an original gray image by using a wavelet transform method; and performing non-local means processing and wavelet reconstruction of each level of components one by one starting from an N-th level, so as to obtain a processed gray image. The image processing method and apparatus can effectively remove various image noises, and well reserve the image details.

The invention discloses an SAR (synthetic aperture radar) despeckle method based on target extraction and three-dimensional block matching denoising, and mainly solves the problem that despeckle effect and detail keeping cannot be considered at one time in the prior art. The implementation steps of the method include: firstly, extracting and removing a target image of an input SAR image; secondly, calculating an iterative initial value, and setting iterative times and iterative step size; thirdly, performing logarithmic transformation to an image after the target image is removed, denoising by the three-dimensional matching denoising algorithm with different standard deviations of noise, and selecting the best noise deviation by evaluating the edge preserving capacity and equivalent number of looks of the denoised image; fourthly, performing three-dimensional matching denoising to a logarithmic image by the best standard deviation of the image; and fifthly, performing exponent transformation to the denoised image, and adding the image with the target image to acquire a final despeckled image. The method has the advantage that image details are saved well, is effective in despeckle, and can be used for SAR image target identification and target detection.

The invention provides an edge detection method of a Canny operator based on high and low thresholds, which mainly comprises the following steps of: 1) carrying out smooth filtering processing on a source image, and removing noise of the image by using switch median filtering instead of Gaussian filtering; (2) calculating the gradient magnitude and direction of the image after smoothing filtering and denoising in the step (1) by adopting a sobel operator; 3) performing non-maximum suppression on the gradient magnitude obtained in the step 2) to obtain an edge image with a single-pixel width; 4) adopting a k-means clustering algorithm to obtain clustering centers of high and low gradient values; 5) obtaining an otsu threshold value of the gradient through an otsu algorithm, and taking a high threshold value and a low threshold value among the high clustering center, the otsu threshold value and the low clustering center; 6) processing the edge image of the single pixel width obtained in the step 3 by using a high threshold value and a low threshold value, and obtaining a binary edge; and 7) removing the interference edge of the binarized edge by adopting an area morphological opening operation, and obtaining a final edge image. The Canny algorithm provided by the invention has the advantages of high positioning precision, strong adaptability, good interference point removal effect and the like.