Topic
Multiresolution analysis
About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.
Papers published on a yearly basis
Papers
More filters
CNET1
TL;DR: Whether the use of those approximations of rational wavelet transforms are efficient within the ARSIS concept is examined, which relies on a particular case: the merging of a 10 m SPOT Panchromatic image and a 30 m Landsat Thematic Mapper multispectral image to synthesize 10m multisectral image TM-HR.
Abstract: The ARSIS concept is designed to increase the spatial resolution of an image without modification of its spectral contents, by merging structures extracted from a higher resolution image of the same scene, but in a different spectral band. It makes use of wavelet transforms and multiresolution analysis. It is currently applied in an operational way with dyadic wavelet transforms that limit the merging of images whose ratio of their resolution is a power of 2. Rational discrete wavelet transforms can be approximated numerically by rational filter banks which would enable a more general merging. Indeed, in theory, the ratio of the resolution of the images to merge is a power of a certain family of rational numbers. The aim of this paper is to examine whether the use of those approximations of rational wavelet transforms are efficient within the ARSIS concept. This work relies on a particular case: the merging of a 10 m SPOT Panchromatic image and a 30 m Landsat Thematic Mapper multispectral image to synthesize 10m multispectral image TM-HR.
62 citations
TL;DR: This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems and addresses the restoration of images with poor spatial correlation as well as strongly correlated (natural) images.
Abstract: This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial correlation as well as strongly correlated (natural) images. The spatial nonlinearity employed in the final estimation step of the Bussgang algorithm is developed according to the minimum mean square error criterion in the case of spatially uncorrelated images. For spatially correlated images, the nonlinearity design is rather conducted using a particular wavelet decomposition that, detecting lines, edges, and higher order structures, carries out a task analogous to those of the (preattentive) stage of the human visual system. Experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are reported.
62 citations
TL;DR: It is shown that a signal generated by the standard state-space stochastic model can be decomposed into innovations at the different sampling frequencies associated to different levels of resolution and that these innovations are all uncorrelated with each other.
61 citations
TL;DR: In this paper, the authors investigated the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France by decomposing the aggregate load into several sub-series with a wavelet transform each component is predicted separately and aggregated to get the final forecast.
61 citations
12 May 2013
TL;DR: This tutorial reviews recent wavelet denoising techniques for medical ultrasound and for magnetic resonance images and evaluates their implementation via MATLAB package and discusses their performances in terms of SNR or PSNR and visual aspects of image quality.
Abstract: In this tutorial, we review recent wavelet denoising techniques for medical ultrasound and for magnetic resonance images. We evaluate their implementation via MATLAB package and discuss their performances in terms of SNR (signal-to-noise ratio) or PSNR (peak signal-to-noise ratio) and visual aspects of image quality. Image denoising using wavelet-based multiresolution analysis requires a delicate compromise between noise reduction and preserving significant image details. Hence, some subtleties associated with these denoising techniques will be explained in detail.
61 citations