JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
High-resolution radar via compressed sensing
IEEE Transactions on Signal Processing
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Adaptive Directional Lifting-Based Wavelet Transform for Image Coding
IEEE Transactions on Image Processing
Weighted Adaptive Lifting-Based Wavelet Transform for Image Coding
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
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Compressive Sensing (CS) provides a new solution to reduce the huge amount of data for the transmission and storage of high resolution synthetic aperture radar (SAR) images. To improve the CS performance, in this work we propose directional lifting wavelet transform (DLWT) as a sparse representation for SAR image CS. Then a Bayesian-based SAR image CS reconstruction algorithm in DLWT domain is proposed. To further improve the reconstruction performance, an accurate prior probability model is proposed which fully exploits interscale attenuation and intrascale directional clustering properties of the DLWT coefficients; and a Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling is employed to recover the image@?s wavelet coefficients and SAR image. Experimental results show that the proposed DLWT Tree-Direction-Clustering Compressive Sensing (DLWT-TDC-CS) can achieve the best reconstruction performance at sampling rates from 0.5 to 0.9 compared with various state-of-the-art CS reconstruction algorithms. DLWT-based CS reconstruction outperforms DWT-based CS reconstruction due to the improved sparse representation.