Results 71 to 80 of about 311,920 (239)
Haar–Fisz Estimation of Evolutionary Wavelet Spectra [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2006 SummaryWe propose a new ‘Haar–Fisz’ technique for estimating the time-varying, piecewise constant local variance of a locally stationary Gaussian time series. We apply our technique to the estimation of the spectral structure in the locally stationary wavelet model. Our method combines Haar wavelets and the variance stabilizing Fisz transform.
Fryzlewicz, Piotr, Nason, Guy P.
openaire +3 more sources
IET Image Processing, Volume 20, Issue 1, January/December 2026.This paper presents a robust reversible watermarking algorithm based on Chebyshev moments, employing a two‐stage embedding mechanism. The proposed method leverages image block partitioning to embed the copyright watermark and the reversible watermark into non‐overlapping regions located inside and outside the inscribed circle of the image, respectively.
Wenjing Sun, Ling Zhang, Hongjun Zhang
wiley +1 more source
Approximate Solutions of Time Fractional Diffusion Wave Models
Mathematics, 2019 In this paper, a wavelet based collocation method is formulated for an approximate solution of (1 + 1)- and (1 + 2)-dimensional time fractional diffusion wave equations.
Abdul Ghafoor +4 more
doaj +1 more source
Haar Wavelet Neural Network Model
2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018 Convolutional neural networks, one of the most important methods of deep learning which is a popular and modern research topic. Nowadays, thismethod has been applied many problems in a short time and obtained successful results for science and the industry.
Pala, T. +4 more
openaire +2 more sources
Feature selection using Haar wavelet power spectrum [PDF]
BMC Bioinformatics, 2006 Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and ...
Sahu Rajendra +2 more
openaire +3 more sources
Multi‐Channel Fusion Residual Network for Robust Bone Fracture Classification From Radiographs
IET Image Processing, Volume 20, Issue 1, January/December 2026.This research introduces a multi‐channel fusion residual network (MFResNet18) to enhance bone fracture classification from radiographs. By integrating a multi‐modal channel filter with multi‐path early feature extraction, the model enriches fracture‐specific details before deep inference. Experimental results demonstrate a classification accuracy of 99.
Sivapriya T +3 more
wiley +1 more source
AGFP: A Deep Attention‐Guided Framework for DWT‐Based Image Steganography
IET Image Processing, Volume 20, Issue 1, January/December 2026.This paper introduces Attention‐Guided Feature Perturbation (AGFP), a novel framework that combines deep learning‐based attention mechanisms with Discrete Wavelet Transform (DWT) embedding for image steganography. By selectively embedding data in perceptually and statistically safe regions, AGFP achieves high imperceptibility, robustness against ...
Taner Cevik +5 more
wiley +1 more source
Fault Strike Detection Using Satellite Gravity Data Decomposition by Discrete Wavelets: A Case Study from Iran [PDF]
Journal of Sciences, Islamic Republic of Iran, 2019 Estimating the gravity anomaly causative bodies boundary can facilitate the gravity field interpretation. In this paper, 2D discrete wavelet transform (DWT) is employed as a method to delineate the boundary of the gravity anomaly sources.
ata eshaghzadeh +2 more
doaj +1 more source
SHAH: SHape-Adaptive Haar Wavelets for Image Processing
, 2016 We propose the shape-adaptive Haar (SHAH) transform for images, which results in an orthonormal, adaptive decomposition of the image into Haar-wavelet-like components, arranged hierarchically according to decreasing importance, whose shapes reflect the ...
P. Fryzlewicz, C. Timmermans
semanticscholar +1 more source
IET Image Processing, Volume 20, Issue 1, January/December 2026.This study presents a robust facial recognition framework based on a unified optimised feature vector that fuses handcrafted descriptors and deep learning embeddings. Using binary grey wolf optimisation for feature selection, the approach reduces redundancy while preserving discriminative power.
Farid Ayeche, Adel Alti
wiley +1 more source