Proceedings ArticleDOI
Compressive Sensing framework for simultaneous compression and despeckling of SAR images
Preetha C,Subrahmanyam Gorthi,Deepak Mishra +2 more
- pp 1-6
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TLDR
A novel CS based technique for simultaneous compression and despeckling of Synthetic Aperture Radar (SAR) images is proposed, incorporating Dual Tree Complex Wavelet Transform based denoising within a sparse regularization frame work and solve by a fast projected Landweber reconstrution algorithm.Abstract:
Recently Compressive Sensing (CS) has shown great potential in the field of Image processing applications. In this paper we propose a novel CS based technique for simultaneous compression and despeckling of Synthetic Aperture Radar (SAR) images. We incorporate Dual Tree Complex Wavelet Transform (DT-CWT) based denoising within a sparse regularization frame work and solve by a fast projected Landweber reconstrution algorithm. The proposed work contributes in three ways. First the processing is done on data acquired in spatial domain under sub Nyquist rate sampling. Then a two stage denoising in the recovery process eliminates speckles in an excellent way, preserving edges and details effectively. Finally simultaneous compression and denoising is achieved in a simple fast and efficient manner saving the computational cost substantially in both time and memory.read more
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Journal ArticleDOI
High-TV Based CS Framework Using MAP Estimator for SAR Image Enhancement
TL;DR: A method using high-order total variation (High-TV) denoising in compressive sensing (CS) framework, based on maximum a posteriori (MAP) estimation to simultaneously denoise and recover the large-size, complex-valued SAR images.
References
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TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
The dual-tree complex wavelet transform
TL;DR: Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual- tree approach.
Journal ArticleDOI
Iterative hard thresholding for compressed sensing
Thomas Blumensath,Michael Davies +1 more
TL;DR: In this paper, the authors present a theoretical analysis of the iterative hard thresholding algorithm when applied to the compressed sensing recovery problem, and show that the algorithm has the following properties.