74results about How to "Preserve texture" patented technology

The invention discloses a natural image denoising method based on dictionary learning and block matching, which mainly solves the problems that texture details are easily lost and homogenous areas are not smooth in the conventional natural image denoising. The method comprises the following steps of: (1) setting a denoising target function and inputting a noise-containing image z(x); (2) making an original image equal to the noise-containing image, namely y(x)=z(x), and making a dictionary D be a redundant discrete cosine transform (DCT) dictionary; (3) updating the atoms of the dictionary D and a corresponding coefficient matrix alphaij by using a kernel-singular value decomposition (KSVD) algorithm; (4) denoising the noise-containing image z(x) by using a block matching three-dimensional (BM3D) algorithm to acquire a primary denoising result; and (5) introducing the updated D and alphaij into the estimation formula of the original image to acquire the denoising result of the noise-containing image z(x). Compared with the conventional classic denoising method, the method achieves a better denoising effect and can be used for denoising a natural image; and the homogeneous area is smoothened, and the texture, the profile and the edge detail information of the image can be maintained at the same time.

A method of enhancing the tonal and spatial characteristics of a digital image, includes receiving a source digital image including a plurality of pixels; producing a tone scale function that can be used to enhance the tonal characteristics of the source digital image; classifying the tone scale function into one of two or more categories, wherein the classification categories include expansive and compressive; and using the tone scale function, the source digital image, and a first spatial filter to produce an enhanced digital image when the tone scale function has been classified as compressive, and using the tone scale function, the source digital image, and a second spatial filter to produce an enhanced digital image when the tone scale function has been classified as expansive.

The invention discloses a multi-sensor image adaptive fusion method based on non-lower sampling contourlet, which mainly aims at solving the problem that the existing image fusion method easily causes distortion in fused images. The fusion process comprises the steps that: the source images are respectively subject to a non-lower sampling contourlet decomposition to obtain low pass subbands and high-frequency directional subbands of the source images in various scales; the Fibonacci method is applied to the obtained low pass subband to find out the optimal low-frequency subband fusion weight; a fusion is adaptively carried out by utilizing the optimal low-frequency subband fusion weight to obtain the low pass subband of the fused image; a fusion is conducted by hiring the high-frequency fusion formula to fuse the high-frequency directional subbands of the source image in various scales so as to obtain the high-frequency directional subbands of the image in various scales; finally, an NSCT inverse transformation is applied to the low pass subbands and the high-frequency directional subbands of the image to be fused to obtain the fused image. The method has the advantages of smoothness, clarity and rich detailed information of the fused image, and thus can be applied to the pre-treatment of remote sensing images and aerial images.

The invention provides a visible light and infrared image fusion algorithm based on non-subsample contourlet transform (NSCT) domain bottom layer visual features.Firstly, visible light and infrared images are subjected to NSCT, high and low frequency subband coefficients of the visible light and the infrared images are obtained, then phase equalization, neighborhood space frequency, neighborhood energy and other information are combined, the pixel active levels of the low frequency subband coefficients are comprehensively measured, fusion weights of the low frequency subband coefficients of the visible light and infrared images are obtained respectively, and therefore low frequency subband coefficients of fusion images are obtained; the pixel active levels of the high frequency subband coefficients are measured through the combination of phase equalization, definition, brightness and other information, fusion weights of the high frequency subband coefficients of the visible light and infrared images are obtained respectively, then high frequency subband coefficients of the fusion images are obtained, finally, NSCT reverse transformation is utilized, and final fusion images are obtained.Detail information of source images can be effectively reserved, and meanwhile useful information of the visible light images and the infrared images is synthesized.

The present invention relates to a three-dimensional model progression transmission method on basis of keeping character, the present invention holds the improvement for character keeping on the basis of the simplified arithmetic of the original three-dimensional model, and at the same time of simplifying the geometrical model, the present invention keeps the topological property and attribute character of the model, and gets the underlying net with smaller data amount, and on the basis of above, the processing coding is held distributed on basis of octree to construct the procession net document. When in transmission, the octree broadness prior coding underlying net is firstly used, and then adopting the procession net transmission method to transport a serial detail recovering information, and continuousely improving model presicion and recovering the original document, thereby solving the problem that: the responding time for the traditional transmission method is long. In adition, the viewpoint related transmission strategy facing the object is designed for the three-dimensional scene, increasing the actural drawing, and making the visibility judging of the object is carried out by putting on the clinet end, and reducing the loading of the server.

The invention discloses a method for denoising a space self-adaption threshold video based on a Surfacelet transform domain, and the method is mainly used for solving the phenomena of unsatisfactory video denoising effects, excessive denoising course complexity, artifact and pseudo Gibbs' effect in a solution course and the like. The method disclosed by the invention is implemented through the following courses: outputting a video to be denoised and carrying out Surfacelet transform; respectively evaluating the noise of factors in the direction subband of each Surfacelet decomposition; computing self-adaption thresholds by utilizing the space energy values of the factors; adjusting the thresholds by utilizing the adjacent domain information of the factors; denoising by utilizing a threshold function; and reconfiguring the denoised factors, thus obtaining the denoised video. Compared with the prior art, the method disclosed by the invention has the advantages of obviously reducing the computing complexity, enhancing the PSNR (Peak Signal to Noise Ratio) value of the denoised video and effectively maintaining the detail information of the video, and can be used for natural video denoising and three-dimensional image denoising.

The invention belongs to the technical field of digital image processing, provides a TV (total variation) image noise removal method based on noise priori constraint and can improve the noise detection accuracy and well protect image structural information. The technical scheme is that the TV image noise removal method based on the noise priori constraint comprises the following steps: step 1) inputting a noise-containing image I; step 2) enabling f to be an original sharp image; step 3) performing impulse noise influence on an image u in transmission and storage processes, and representing an image polluted by mixed noise with g; step 4) estimating noise positions with an ROAD (rank-ordered absolute difference) statistical method; step 5) constructing a TV-ROAD iteration noise removal model; step 6) solving an equation shown in the specification according to a noise removed image f obtained in the previous step to obtain a two-value matrix vector b. The method is mainly applied to digital image processing.