A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Handbook of pattern recognition & computer vision
Handbook of pattern recognition & computer vision
A practical approach to fractal-based image compression
Digital images and human vision
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Efficient Context-Based Entropy Coding Lossy Wavelet Image Compression
DCC '97 Proceedings of the Conference on Data Compression
Estimation of entropy and mutual information
Neural Computation
Context Modeling and Entropy Coding of Wavelet Coefficients for Image Compression
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
Model-based compressive sensing
IEEE Transactions on Information Theory
CoSaMP: iterative signal recovery from incomplete and inaccurate samples
Communications of the ACM
High-resolution hyperspectral imaging via matrix factorization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
Multiscale modeling and estimation of Poisson processes with application to photon-limited imaging
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Comparison of different methods of classification in subband coding of images
IEEE Transactions on Image Processing
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
Image coding by block prediction of multiresolution subimages
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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In recent years, the theory of compressed sensing has emerged as an alternative for the Shannon sampling theorem, suggesting that compressible signals can be reconstructed from far fewer samples than required by the Shannon sampling theorem. In fact the theory advocates that nonadaptive, “random” functionals are in some sense optimal for this task. However, in practice, compressed sensing is very difficult to implement for large data sets, particularly because the recovery algorithms require significant computational resources. In this work, we present a new alternative method for simultaneous image acquisition and compression called adaptive compressed sampling. We exploit wavelet tree structures found in natural images to replace the “universal” acquisition of incoherent measurements with a direct and fast method for adaptive wavelet tree acquisition. The main advantages of this direct approach are that no complex recovery algorithm is in fact needed and that it allows more control over the compressed image quality, in particular, the sharpness of edges. Our experimental results show, by way of software simulations, that our adaptive algorithms perform better than existing nonadaptive methods in terms of image quality and speed.