A method and apparatus for performing pulse shaping using different windowing functions for different sub-bands of a transmission is disclosed. A method for use in a wireless transmit/receive unit (WTRU) may include the WTRU receiving data symbols. The WTRU may assign the data symbols to a plurality of subcarriers in different sub-bands and map the data symbols on each of the plurality of subcarriers in the different sub-bands to a plurality of corresponding subcarriers of an inverse fast Fourier transform (IFFT) block. The WTRU may take an IFFT of the block for each sub-band and pad an output of the IFFT block with a prefix and a postfix for each sub-band. The WTRU may apply a windowing function to an output of the padding for each sub-band and form a composite signal for transmission by adding an output of the windowing of each sub-band. The WTRU may transmit the signal.

Pulse-shaped orthogonal frequency division multiplexing

The invention discloses a denoising method for an image with mixed noises, which comprises the following steps of: performing wavelet packet decomposition on the noise-including image; converting the image subjected to the wavelet packet decomposition to a wavelet domain, and dividing the image into a low-frequency sub-band image and a high-frequency sub-band image; filtering the low-frequency sub-band image adopting a neighborhood averaging method; and firstly detecting satisfied special points in the noise of the high-frequency sub-band image by using a cluster analysis method, filtering to remove pulse noise by adopting median filter, and denoising white Gaussian noise on the image by adopting threshold processing in a wavelet contraction step according to the equivalence of a Gaussian curvature high-order diffusion of and wavelet contraction method. When the denoising method is adopted, the mixed noises in the image can be removed, and the double functions of high-frequency characteristics and edge shapes can be well kept, thus a better effect is kept.

Denoising method for image with mixed noises

The invention provides an image denoising method based on a self-adaptive wavelet threshold and a two-sided filter and aims to improve the effect of a wavelet threshold denoising algorithm and better protect the edge and the detailed information of an image. The algorithm comprises the following steps of decomposing the image by adopting a discrete wavelet to obtain a plurality of sub-bands and the wavelet coefficients of the sub-bands; selecting a threshold which is self-adaptively changed along with the changes of wavelet-decomposing scales and the sub-bands, and carrying out quantitative threshold processing by adopting a soft threshold function; carrying out inverse wavelet transformation to obtain a reconstructed image; filtering the reconstructed image by adopting the two-sided filter so as to obtain the clear image. According to the image denoising method, wavelet threshold denoising is carried out by utilizing the threshold which is self-adapted to the wavelet-decomposing scales and the sub-bands, and filtering is carried out by combining the two-sided filter, so that through the designed denoising algorithm, not only can white gaussian noise be effectively removed, but also the edge and the detailed information of the image can be well reserved.

Image denoising method based on self-adaptive wavelet threshold and two-sided filter

The invention discloses a natural image noise removal method based on dual redundant dictionary learning, aiming at mainly solving the problem that texture details are easy to lose in the existing natural image noise removal method. The natural image noise removal method based on dual redundant dictionary learning comprises the following steps: (1) inputting an image Y to be subjected to noise removal; (2) unfolding the image Y to be subjected to noise removal under a multi-scale redundant stable wavelet dictionary R to obtain coefficient components betaj of the image Y on different scales, wherein j is 1, 2,... and N; (3) initializing each variable to make a dictionary Dj be a redundant dictionary DCT, and updating atoms and corresponding coefficient matrixes of the dictionary Dj by adopting the KSVD algorithm; (4) calculating the estimation value of the noise removal results of all the coefficient components betaj; and (5) carrying out multi-scale redundant reverse transformation onall the coefficient components betaj subjected to noise removal to obtain the noise removal result of the image Y to be subjected to noise removal. Compared with the existing classical noise removal method, the natural image noise removal method based on dual redundant dictionary learning can better keep the texture detail information in the image to be subjected to noise removal and can be used for natural image noise removal.

Natural image noise removal method based on dual redundant dictionary learning

The invention discloses a Chinese medicinal material microscopic image feature extraction and recognition retrieval method. A PCNN time matrix information processing method is introduced, image feature information in a transform domain is extracted through combination of PCNN and image Fourier transform and decimal power exponent filtering phase, and a Chinese medicinal material microscopic image information rapid storage and recognition retrieval method is put forward on the basis of mahalanobis distance with combination of comprehensive similarity measure of a Pearson product moment correlation method so that a holographic Chinese medicinal material microstructure image and pollen image feature extraction and recognition retrieval system is constructed, objectivity, accuracy, repeatability and intelligent degree of Chinese medicinal material quality evaluation can be further enhanced, and a new approach is provided for modernization of Chinese medicinal material detection and analysis.

Chinese medicinal material microscopic image feature extraction and recognition retrieval method

The invention relates to a method for denoising wavelet images chosen on the basis of the neighbor data of a sliding window. The method comprises the following steps: step 1, decomposing a noisy image into sub-bands by wavelet transformation processing; step 2, processing the wavelet coefficient of each sub-band according to the following steps: 1), carrying out threshold judgment of the center wavelet coefficient of each neighbor centering on each wavelet coefficient in each sub-band, comparing the correlativity coefficient Theta of each neighbor of the coefficient, and carrying out 2) if the maximal correlativity coefficient Theta is higher than an empirical value, or carrying out step 3 directly if the maximal correlativity coefficient Theta is lower than the empirical value; 2), calculating the Bayes adaptive threshold value of the threshold processing window chosen in 1) so as to obtain a scaling factor; and 3), scaling the wavelet coefficient in the center of the window according to the scaling factor; and step 3, reconstructing the wavelet coefficient so as to obtain the filtered image after processing each sub-band of the wavelet with adaptive sliding window neighbor wavelet process. The method has the advantages of higher peak value signal-to-noise ratio and better protection effect for image edges.

Wavelet image denoising process based on sliding window adjacent region data selection

The invention relates to a method for denoising wavelet images chosen on the basis of the neighbor data of a self-adapting sliding window. The method comprises the following steps: step 1, decomposing a noisy image into sub-bands by wavelet transformation processing; step 2, processing the wavelet coefficient of each sub-band separately according to the following steps: 1), carrying out thresholdjudgment of the center wavelet coefficient of each neighbor centering on each wavelet coefficient in each sub-band, comparing the correlativity coefficient Theta of each neighbor of the coefficient, and setting a sliding window, in which the maximal correlativity coefficient Theta is located, as the window for threshold processing; and 2), calculating the Bayes adaptive threshold value of the threshold processing window chosen in 1) so as to obtain a scaling factor; and scaling the wavelet coefficient in the center of the window according to the scaling factor; and step 3, reconstructing the wavelet coefficient so as to obtain the filtered image after processing each sub-band of the wavelet with adaptive sliding window neighbor wavelet process. The method achieves a higher peak value signal-to-noise ratio, thereby having a better protection effect for image edges.

Wavelet image denoising process based on adaptive sliding window adjacent region threshold

The method relates to a rotation inertia measuring method, in particular to a method for measuring rotation inertia of a three-wire pendulum. The method comprises the following steps: rotating a preliminary swing angle of the three-wire pendulum to a certain angle; causing the three-wire pendulum to swing freely, recording the number of swings n and the swing time t, calculating the swing period T (T= t/n), and obtaining the rotation inertia of a lower round disk m by utilizing a formula J = (mgRrT ) / (4Pi L); placing a measured rigid body with a mass of M on the lower round disk, causing a rotating shaft of the measured rigid body to be coincident with a OO' shaft, obtaining the rotation inertia of the measured rigid body according to a formula J = [(M+m)gRrT ] / (4Pi L), and working out the rotation inertia of the measured rigid body rotating around the central rotating shaft OO' shaft utilizing a formula J = (gRr) / (4Pi L) [(M+m) T -mT ]. The method breaks the restriction of original calculation formulae and creatively put forwards swing angle limit, thereby providing a better principle for measuring the rotation inertia of the three-wire pendulum and further improving the measurement accuracy.

Method for measuring rotation inertia of three-wire pendulum

The invention belongs to the technical field of image processing, relating to a Neigh Shrink image denoising method based on PCNN (Pulse Coupled Neural Network) region segmentation. The Neigh Shrink image denoising method comprises the steps of: carrying out two-dimensional stable wavelet transformation on a noisy image f (x,y), and respectively acquiring subband coefficients: low-frequency coefficients, horizontal detail coefficients, vertical detail coefficients and diagonal detail coefficients; carrying out region segmentation on the first layer of the low-frequency coefficients by utilizing a PCNN, and recording image information obtained after segmentation as the domain PCNN; determining image neighborhood domain Neigh Shrink according to a Neigh Shrink method; setting dm and n as the image coefficients of the current threshold values, and obtaining the neighborhood required by the processing of the current threshold values by utilizing dm, n belongs to a set of the intersection of (domainPCNN) and (domainNeighShrink); keeping the low-frequency coefficients unchanged, and carrying out Neigh Shrink neighborhood threshold value processing respectively on each layer of the horizontal detail coefficients, the vertical detail coefficients and the diagonal detail coefficients obtained in the last step; and carrying out stable wavelet reconstruction on the low-frequency coefficients and filtered high-frequency subbands to obtain the denoised images. The invention can better restore original images, protect marginal information, and improve the denoising performance.

Neigh Shrink image denoising method based on PCNN (Pulse Coupled Neural Network) region segmentation

The invention belongs to the technical field of image processing and relates to an image de-noising method based on PCNN region segmentation The method comprises the following steps: performing two-dimensional stationary wavelet transform on a noise image f(x,y) to obtain subband coefficients including low-frequency coefficient, level detail coefficient, vertical detail coefficient and diagonal detail coefficient; performing region segmentation on the low-frequency coefficient of the first layer by using PCNN, connecting the directly-connected image regions into a closed region and recordingthe image information obtained by segmenting as template; maintaining the low-frequency coefficient and performing NeighShrink processing on each coefficient in the neighborhood of the level detail coefficient, the vertical detail coefficient and the diagonal detail coefficient using the image coefficient in the template marked as the same closed neighborhood; and performing stationary wavelet reconstruction on the low-frequency coefficient and the filtered high-frequency subband to obtain the de-noising image * In the invention, the interlayer relevance of the wavelet coefficient is fully considered, thereby better recovering the initial image, protecting the edge information and improving the de-noising performance

Image de-noising method based on PCNN region segmentation

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaolin Gong

Author Tools

Chat about Author