matlab-wavelet-toolbox-v2

name

matlab-wavelet-toolbox-v2

description

MATLAB Wavelet Toolbox. Functions - wavedec2, waverec2, dwt2, idwt2, swt2, lwt2, ilwt2, wdenoise2, dualtree2, idualtree2, shearletSystem, liftingScheme, liftingStep, addlift, wfilters, wmaxlev, dldwt, dlidwt, cwtLayer, appcoef2, detcoef2. Tasks - decompose an image into frequency bands, denoise a medical image using wavelets, remove noise from MRI or CT or ultrasound, extract texture features at multiple scales, design a custom wavelet, learn wavelets from data, detect edges and orientations, analyze directional structures like vessels or fibers, fuse multi-modal images, compress an image with wavelets, build wavelet layers for deep learning, choose the right wavelet for my image type, verify perfect reconstruction. Domains - MRI denoising, CT noise reduction, ultrasound speckle removal, image fusion, texture classification, wavelet-based feature extraction, medical image preprocessing, image compression.

MATLAB Wavelet Toolbox Skill

Expert skill for 2D wavelet analysis in MATLAB. Focus areas: custom wavelet design via lifting schemes, medical image analysis (MRI/CT/ultrasound), and deep learning integration.

Read Before Coding

Open the relevant knowledge card before writing code:

Task	Knowledge File
Mathematical foundations (MRA, Daubechies, PR conditions)	knowledge/mathematical-foundations.md
Custom wavelet design / learning wavelets from data	knowledge/cards/custom-wavelets.md
MRI/CT/ultrasound processing	knowledge/cards/medical-imaging.md
Choosing which wavelet for your image type	knowledge/cards/filters.md
When to use which 2D transform	knowledge/cards/2d-transforms.md
Denoising method selection & recipes	knowledge/cards/denoising.md
Directional edge detection guidance	knowledge/cards/dual-tree.md
Curvilinear features (vessels/fibers)	knowledge/cards/shearlets.md
Deep learning + wavelet integration patterns	knowledge/cards/deep-learning.md

Critical Rules

1. Check level limit: wmaxlev(size(img), wname) before decomposition
2. Match transform pairs: dwt2/idwt2, wavedec2/waverec2, lwt2/ilwt2
3. Boundary mode: Use 'symmetric' for medical images
4. For custom wavelets: Verify perfect reconstruction, vanishing moments

Transform Selection

Analysis type?
├── Standard 2D decomposition
│   ├── Critically sampled → wavedec2/waverec2
│   └── Shift-invariant → swt2/iswt2 (MODWT is 1D only)
├── Directional analysis
│   ├── 6 orientations → dualtree2/idualtree2
│   └── Curvilinear (vessels) → shearletSystem
├── Custom wavelet design
│   └── Lifting scheme → liftingScheme + lwt2/ilwt2
└── Time-frequency (signals)
    └── CWT → cwt/icwt

Wavelet Selection for Images

Image Type	Wavelet	Rationale
Medical (MRI, CT)	db4-db8	Good edge preservation
Smooth gradients	sym6-sym8	Higher vanishing moments
Sharp edges	db2-db4	Shorter filters
Compression	bior4.4, bior6.8	Symmetric (linear phase)

Quick Patterns

Multi-level Decomposition

[C, S] = wavedec2(img, 4, 'db4');
cA = appcoef2(C, S, 'db4');           % Approximation
cH = detcoef2('h', C, S, 1);          % Horizontal detail, level 1
imgRec = waverec2(C, S, 'db4');

Medical Image Denoising

% MRI (Rician noise)
mriClean = wdenoise2(mri, 'DenoisingMethod', 'Bayes', ...
    'Wavelet', 'sym4', 'Level', 4);
% Valid methods: 'UniversalThreshold', 'Minimax', 'SURE', 'Bayes', 'FDR'

% Ultrasound (multiplicative speckle)
logUS = log(1 + double(us));
denoised = wdenoise2(logUS, 'DenoisingMethod', 'Bayes');
usClean = exp(denoised) - 1;

Custom Wavelet via Lifting

ls = liftingScheme('Wavelet', 'haar');
ls = addlift(ls, liftingStep('Type', 'predict', ...
    'Coefficients', [-0.5, 0.5], 'MaxOrder', 1));
ls = addlift(ls, liftingStep('Type', 'update', ...
    'Coefficients', [0.25, 0.25], 'MaxOrder', 0));
[LL, LH, HL, HH] = lwt2(img, liftingScheme=ls);

Directional Analysis

[a, d] = dualtree2(img, Level=3);
% d{level}(:,:,dir) for 6 directions: ±15°, ±45°, ±75°

Deep Learning (R2025a+)

x = dlarray(img, 'SSCB');
[A, D] = dldwt(x, Wavelet='db4');  % A=approx, D=detail (concatenated)
% For 2D: D(:,:,1,:)=H, D(:,:,2,:)=V, D(:,:,3,:)=D subbands
xRec = dlidwt(A, D, Wavelet='db4');  % Inverse transform

Template Scripts

Ready-to-use .m files in scripts/ -- read and adapt for your task:

Template	Use Case
template_mri_denoising.m	MRI Rician noise removal pipeline
template_ct_denoising.m	CT Poisson noise with Anscombe transform
template_ultrasound_speckle.m	Ultrasound speckle reduction (log-domain)
template_multiresolution_analysis.m	Standard wavedec2 decomposition and visualization
template_dual_tree_directional.m	Directional edge detection with dualtree2
template_shearlet_curvilinear.m	Vessel/fiber detection with shearlets
template_custom_lifting_wavelet.m	Custom wavelet design via liftingScheme
template_deep_learning_wavelet.m	Wavelet layers for deep learning (dldwt)
template_image_fusion.m	Multi-modal image fusion (MRI+CT, PET+CT)
template_wavelet_feature_extraction.m	Wavelet energy/entropy features for classification

Knowledge Base

Unique content not in the model's training data -- prioritize reading:

• mathematical-foundations.md: MRA theory, Daubechies construction, spectral factorization, perfect reconstruction, regularity, admissibility
• custom-wavelets.md: Lifting scheme theory, learnable wavelet constraints, verification
• medical-imaging.md: Noise models (Rician, Poisson, speckle), fusion rules, feature extraction

Knowledge cards provide when-to-use guidance and decision frameworks for choosing transforms, wavelets, and methods.

See knowledge/INDEX.md for full navigation.