Results 31 to 40 of about 566 (171)
Digital Shearlet Transforms [PDF]
Over the past years, various representation systems which sparsely approximate functions governed by anisotropic features such as edges in images have been proposed. We exemplarily mention the systems of contourlets, curvelets, and shearlets. Alongside the theoretical development of these systems, algorithmic realizations of the associated transforms ...
Gitta Kutyniok +2 more
openaire +3 more sources
The Continuous Shearlet Transform in Arbitrary Space Dimensions [PDF]
This note is concerned with the generalization of the continuous shearlet transform to higher dimensions. Similar to the two-dimensional case, our approach is based on translations, anisotropic dilations and specific shear matrices. We show that the associated integral transform again originates from a square-integrable representation of a ...
Dahlke, Stephan +2 more
openaire +4 more sources
Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform
Model-based image denoising methods are well suited for use as image processors in remote sensing systems such as satellites due to their well-developed mathematical theory and low computational cost, but these methods can often only deal with a single ...
Libo Cheng, Pengyu Chen
doaj +1 more source
Linearized Riesz transform and quasi-monogenic shearlets [PDF]
The only quadrature operator of order two on L2(ℝ2) which covaries with orthogonal transforms, in particular rotations is (up to the sign) the Riesz transform. This property was used for the construction of monogenic wavelets and curvelets. Recently, shearlets were applied for various signal processing tasks.
Sören Häuser +2 more
openaire +2 more sources
Expectation‐maximization algorithm generative adversarial network (EMA‐GAN) is proposed to fuse images from different modalities. This is an EM learning framework based on GAN that maximizes the likelihood of fused results and estimates potential variables.
Xiuliang Xi +5 more
wiley +1 more source
Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
Abstract Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge.
Mehdi Gheisari +10 more
wiley +1 more source
Otherness feature extraction method for underground image based on Shearlet transform
For the problem that face images collected underground are susceptible to dust interference and most feature extraction methods are sensitive to noise, an otherness feature extraction method for underground image based on Shearlet transform was proposed.
HUANG Yu, ZHANG Yingjun, PAN Lihu
doaj +1 more source
River boundary detection and autonomous cruise for unmanned surface vehicles
The detection of river boundaries is important for judging the drivable area of USVs, and it can also be utilized to ensure driving safety by limiting the effective drivable areas of the USVs in the river areas. This work proposes a real‐time detection method for river boundaries based on a LiDAR sensor to detect the boundaries of incompletely ...
Kai Zhang +5 more
wiley +1 more source
Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning
The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading.
Hadi Rezaeilouyeh, Mohammad H. Mahoor
doaj +1 more source
GF-3 SAR IMAGE DESPECKLING BASED ON THE IMPROVED NON-LOCAL MEANS USING NON-SUBSAMPLED SHEARLET TRANSFORM [PDF]
GF-3 synthetic aperture radar (SAR) images are rich in information and have obvious sparse features. However, the speckle appears in the GF-3 SAR images due to the coherent imaging system and it hinders the interpretation of images seriously.
R. Shi, Z. Sun
doaj +1 more source

