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and edge detection using anisotropic diffusion","{\"openalex\":\"W2150134853\",\"doi\":\"10.1109/34.56205\"}","openalex","2026-05-14T00:41:05.394055923","2026-05-21T02:19:02.26827264","doi","10.1109/34.56205","arxivId","openalexId","W2150134853","title","abstractText","A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image.< >","publishedDate","1990-07-01","publishedYear",1990,"venue","IEEE Transactions on Pattern Analysis and Machine Intelligence","citationCount",12010,"authors","keywords",[140,141,30],"pdfUrl","topVenue","Medical Image Segmentation Techniques","Image Retrieval and Classification Techniques","paper_W2001141328","A Global Geometric Framework for Nonlinear Dimensionality Reduction","{\"openalex\":\"W2001141328\",\"doi\":\"10.1126/science.290.5500.2319\"}","2026-05-14T00:40:02.191705794","2026-05-21T02:16:23.212657251","10.1126/science.290.5500.2319","W2001141328","Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.","2000-12-22",2000,"Science",13694,[30,155,156],"Blind Source Separation Techniques","Remote-Sensing Image Classification","paper_W2034139177","A Practical Guide to Wavelet Analysis","{\"openalex\":\"W2034139177\",\"doi\":\"10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2\"}","2026-05-14T00:38:52.362263543","2026-05-21T02:14:23.185965199","10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2","W2034139177","A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El Niño–Southern Oscillation (ENSO). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmöller, cross-wavelet spectra, and coherence are described. The statistical significance tests are used to give a quantitative measure of changes in ENSO variance on interdecadal timescales. Using new datasets that extend back to 1871, the Niño3 sea surface temperature and the Southern Oscillation index show significantly higher power during 1880–1920 and 1960–90, and lower power during 1920–60, as well as a possible 15-yr modulation of variance. The power Hovmöller of sea level pressure shows significant variations in 2–8-yr wavelet power in both longitude and time.","1998-01-01",1998,"Bulletin of the American Meteorological Society",14778,[30,171,172],"https://journals.ametsoc.org/downloadpdf/journals/bams/79/1/1520-0477_1998_079_0061_apgtwa_2_0_co_2.pdf","Mathematical Analysis and Transform Methods","Seismic Imaging and Inversion Techniques","paper_W2062024414","Ten Lectures on Wavelets","{\"openalex\":\"W2062024414\",\"doi\":\"10.1063/1.4823127\"}","2026-05-14T00:38:31.273980565","2026-05-21T02:13:42.18405036","10.1063/1.4823127","W2062024414","Mathematics of Computing -- Miscellaneous.","1992-01-01",1992,"Computers in Physics",15383,[30,172],"paper_W2103559027","Nonlinear total variation based noise removal algorithms","{\"openalex\":\"W2103559027\",\"doi\":\"10.1016/0167-2789(92)90242-f\"}","2026-05-14T00:38:27.266289885","2026-05-21T02:13:20.211759355","10.1016/0167-2789(92)90242-f","W2103559027","1992-11-01","Physica D Nonlinear Phenomena",15592,[30,140,197],"Advanced Image Processing Techniques","paper_W2145096794","Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information","{\"openalex\":\"W2145096794\",\"doi\":\"10.1109/tit.2005.862083\"}","2026-05-14T00:38:19.408861123","2026-05-21T02:13:10.195315801","10.1109/tit.2005.862083","W2145096794","This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/. Is it possible to reconstruct f from the partial knowledge of its Fourier coefficients on the set /spl Omega/? A typical result of this paper is as follows. Suppose that f is a superposition of |T| spikes f(t)=/spl sigma//sub /spl tau//spl isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl Omega/| for some constant C/sub M/>0. We do not know the locations of the spikes nor their amplitudes. Then with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the /spl lscr//sub 1/ minimization problem. In short, exact recovery may be obtained by solving a convex optimization problem. We give numerical values for C/sub M/ which depend on the desired probability of success. Our result may be interpreted as a novel kind of nonlinear sampling theorem. In effect, it says that any signal made out of |T| spikes may be recovered by convex programming from almost every set of frequencies of size O(|T|/spl middot/logN). Moreover, this is nearly optimal in the sense that any method succeeding with probability 1-O(N/sup -M/) would in general require a number of frequency samples at least proportional to |T|/spl middot/logN. The methodology extends to a variety of other situations and higher dimensions. For example, we show how one can reconstruct a piecewise constant (one- or two-dimensional) object from incomplete frequency samples - provided that the number of jumps (discontinuities) obeys the condition above - by minimizing other convex functionals such as the total variation of f.","2006-01-25",2006,"IEEE Transactions on Information Theory",15738,[211,30,212],"Sparse and Compressive Sensing Techniques","Medical Imaging Techniques and Applications","paper_W4250955649","Compressed sensing","{\"openalex\":\"W2296616510\"}","2026-05-14T00:30:26.406231023","2026-05-21T02:10:51.176521715","10.1109/tit.2006.871582","W2296616510","Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0>","1989-07-01",1989,20987,[30,264,265],"Advanced Image Fusion Techniques","Statistical and numerical algorithms",{"_58":267,"_60":268},[],[269],{"_63":270,"_65":271,"_67":272,"_69":273},1780951854734,{"_72":275,"_74":75,"_76":270,"_77":-5,"_78":26,"_79":26,"_80":26,"_81":-5,"_82":-5,"_83":84,"_35":85,"_86":87},[71,274],"[\"portal\",\"industries\"]","industries",[276,277,278,279,280,281,282,283,284,285,286,287],{"_15":348,"_29":349,"_31":-5,"_32":350,"_33":351,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":352,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":349,"_57":351},{"_15":344,"_29":345,"_31":-5,"_32":-5,"_33":346,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":347,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":345,"_57":346},{"_15":338,"_29":339,"_31":-5,"_32":340,"_33":341,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":342,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":339,"_57":341},{"_15":334,"_29":335,"_31":-5,"_32":-5,"_33":336,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":337,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":335,"_57":336},{"_15":330,"_29":331,"_31":-5,"_32":-5,"_33":332,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":333,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":331,"_57":332},{"_15":324,"_29":325,"_31":-5,"_32":326,"_33":327,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":328,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":325,"_57":327},{"_15":319,"_29":320,"_31":-5,"_32":-5,"_33":321,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":322,"_300":26,"_301":323,"_302":26,"_18":-5,"_56":320,"_57":321},{"_15":315,"_29":316,"_31":-5,"_32":-5,"_33":317,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":318,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":316,"_57":317},{"_15":311,"_29":312,"_31":-5,"_32":-5,"_33":313,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":314,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":312,"_57":313},{"_15":307,"_29":308,"_31":-5,"_32":-5,"_33":309,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":310,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":308,"_57":309},{"_15":303,"_29":304,"_31":-5,"_32":-5,"_33":305,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":306,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":304,"_57":305},{"_15":288,"_29":289,"_31":-5,"_32":-5,"_33":290,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":291,"_42":-5,"_44":-5,"_292":293,"_294":-5,"_295":75,"_296":297,"_298":299,"_300":26,"_301":84,"_302":26,"_18":-5,"_56":289,"_57":290},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[329],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[343],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[353,354],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_357":24},"SITE_BASE_URL"]

Image and Signal Denoising Methods 技术

Image and Signal Denoising Methods · 衍生技术