Overcomplete Wavelets for Compressed Sensing
Bhabesh Deka
TL;DR
This paper evaluates various overcomplete wavelet transforms for compressed sensing, focusing on their effectiveness in image recovery from highly undersampled and noisy measurements in different domains.
Contribution
It systematically compares multiple overcomplete wavelet transforms to identify the most effective for compressed sensing image reconstruction.
Findings
Certain transforms outperform others in noisy conditions
Performance varies significantly with measurement accuracy
Optimal transform choice depends on domain and measurement quality
Abstract
Compressed sensing (CS) using overcomplete wavelet dictionaries has been a well-investigated topic in the recent times for image and vision applications. In this paper, different overcomplete wavelet transforms have been studied to estimate the best transform. Performance evaluations are carried out for different overcomplete wavelet transforms from highly undersampled and inaccurate measurements for the recovery of images in frequency as well as physical domains.
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Image and Signal Denoising Methods · Blind Source Separation Techniques