82 results about "Anisotropic diffusion filtering" patented technology

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on a neighborhood logarithm specific value and anisotropic diffusion, relating to the field of remote sensing image processing and mainly solving the problem that a difference graph structure of SAR image change detection is seriously influenced by SAR image spot noises. The SAR image change detection method comprises the following steps: (1) structuring a difference striograph IL of two images I1 and I2 of different times and same terrain according to a neighborhood logarithm specific value method; (2) carrying out self-adaptation window anisotropic diffusion filtering processing on the difference striograph IL to obtain a final filtering result graph NI<t>[L] of the difference striograph; and (3) carrying out threshold segmentation on the final filtering result graph NI<t>[L] of the difference striograph by using an OSTU (Maximum Between-Class Variance) threshold algorithm to obtain a change detection result graph CNI<t>[L] for structuring the difference striograph by using the neighborhood logarithm specific value method. The histogram of the difference striograph can be compressed so as to effectively eliminate miscellaneous points in the change detection result graph; and the self-adaptation window anisotropic diffusion filtering has favorable edge retentiveness and cannot blurs the edges of the image, thus, an obtained change detection result graph is finer.

A method for reducing noise in a computed tomographic (CT) image includes acquiring both a first set of projection views and a second set of projection views, wherein for each projection view in the first set of projection views there is an associated projection view in the second set of projection views representing the same object scanned at substantially the same time from substantially the same position. The method further includes reconstructing the first set of projection views and the associated second set of projection views to obtain a first image and a second image, respectively. Next, the first image and the second image are combined to obtain a noise map and an amount of noise in a product image is estimated utilizing the noise map. The method also includes filtering using the noise map to perform noise reduction.

A computer system for registering synthetic aperture radar (SAR) images includes a database for storing SAR images to be registered, and a processor for registering SAR images from the database. The registering includes selecting first and second SAR images to be registered, individually processing the selected first and second SAR images with an anisotropic diffusion algorithm, and registering the first and second SAR images after the processing. A shock filter is applied to the respective first and second processed SAR images before the registering. Elevation data is extracted based on the registered SAR images.

Methods and systems for extracting formation properties from formation logging data is disclosed. A method includes obtaining a deconvolution filter; and processing the formation logging data using the deconvolution filter to produce estimates of the formation properties. The estimates of the formation properties may be further processed with a diffusion filter, which may be an anisotropic diffusion filter. A system for extracting formation properties from formation logging data includes a central processing unit and a memory, wherein the memory stores a program having instructions for performing a method that includes designing a deconvolution filter; and processing the formation logging data using the deconvolution filter to produce estimates of the formation properties.

For the purpose of conducting anisotropic diffusion filtering in a shorter time, an image processing method for conducting anisotropic diffusion filtering on a pixel value I i, j in a two-dimensional image, comprises: finding conduction coefficients C n , C s , C w , C e , C nw , C sw , C ne and C se in eight surrounding directions for each pixel based on a pixel value gradient ˆ‡I to produce a two-dimensional distribution image of the conduction coefficients; and finding pixel value first partial differentials ˆ‡ n I, ˆ‡ s I, ˆ‡ w I, ˆ‡ e I, ˆ‡ nw I, ˆ‡ sw I, ˆ‡ ne I and ˆ‡ se I in the eight surrounding directions for each pixel to calculate a pixel value in an output image according to: I i,j n +1 = I i,j n +»[[ C n �¢ ·ˆ‡ n I + C s �¢ ·ˆ‡ s I + C w �¢ ·ˆ‡ w I + C e �¢ ·ˆ‡ e I ] + 1 2 [ C nw �¢ ·ˆ‡ nw I + C sw �¢ ·ˆ‡ sw I + C ne �¢ ·ˆ‡ ne I + C nw �¢ ·ˆ‡ nw I ]] i,j n , (n: the number of times of repetition, »: a constant).

A computer system for processing synthetic aperture radar (SAR) images includes a database for storing SAR images to be processed, and a processor for processing a SAR image from the database. The processing includes determining noise in a SAR image to be processed, selecting a noise threshold for the SAR image based on the determined noise, and mathematically adjusting an anisotropic diffusion algorithm based on the selected noise threshold. The adjusted anisotropic diffusion algorithm is applied to the SAR image.

The invention discloses a head MRI image skull peeling module. The head MRI image skull peeling module comprises an anisotropy diffusion filtering unit, an Otsu binary unit, a Canny edge detection unit and an image segmentation unit, wherein the anisotropy diffusion filtering unit is used for receiving head MRI images and carrying out smooth image filtering, and image edges are kept during image smoothing; the Otsu binary unit is used for carrying out binary segmentation of a smooth image through Otsu calculation, and a binary image is acquired; the Canny edge detection unit is utilized for carrying out canny operator operation to draft all contour lines of the smooth image to generate a contour image; the image segmentation unit is used for calculating ratios of pixel points of each contour line of the contour image to an area of the binary image, whether corresponding areas of an original image are eliminated is determined according to the ratios, so skull peeling from the head MRI image is carried out, and micro noise is further eliminated. The head MRI image skull peeling module is advantaged in that image processing accuracy is improved, and image processing calculation mount is further reduced.

The invention discloses an image haze removal method based on a pixel dark channel and anisotropic diffusion filtering, belonging to the technical field of the image haze removal method. The technical key point is that the image haze removal method comprises the following steps of: (1) calculating a dark channel Idark(x) of each pixel point of haze image I(x); (2) calculating an atmospheric light intensity value A according to the dark channel Idark(x) of each pixel point; (3) performing anisotropic diffusion filtering on the dark channel Idark(x) of each pixel point; and (4) calculating transmittance t(x) of each pixel point according to a transmittance calculating formula; and (5) performing image restoration processing according to the atmospheric light intensity value A and the transmittance t(x). The invention aims at providing the image haze removal method, which is relatively small in calculated amount, less in stored resources occupied and quick in processing speed, based on pixel dark channel and anisotropic diffusion filtering. The image haze removal method is used for image haze removal processing.

Multiresolution decomposition of image data before scan conversion processing is hierarchically performed, low-frequency decomposed image data and high-frequency decomposed image data with first to n-th levels are acquired, nonlinear anisotropic diffusion filtering is performed on output data from a next lower layer or the low-frequency decomposed image data in a lowest layer, and filtering for generating edge information on a signal for every layer is performed from the output data from the next lower layer or the low-frequency decomposed image data in the lowest layer. In addition, on the basis of the edge information on each layer, a signal level of the high-frequency decomposed image data is controlled for every layer and multiresolution mixing of the output data of the nonlinear anisotropic diffusion filter and the output data of the high-frequency level control, which are obtained in each layer, are hierarchically performed.