Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Compressed sensing for digital holographic microscopy
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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In fluorescence microscopy the noise level and the photobleaching are cross-dependent problems since reducing exposure time to reduce photobleaching degrades image quality while increasing noise level. These two problems cannot be solved independently as a post-processing task, hence the most important contribution in this work is to a-priori denoise and reduce photobleaching simultaneously by using the Compressed Sensing framework (CS). In this paper, we propose a CS-based denoising framework, based on statistical properties of the CS optimality, noise reconstruction characteristics and signal modeling applied to microscopy images with low signal-tonoise ratio (SNR). Our approach has several advantages over traditional denoising methods, since it can under-sample, recover and denoise images simultaneously. We demonstrate with simulated and practical experiments on fluorescence image data that thanks to CS denoising we can obtain images with similar or increased SNR while still being able to reduce exposition times.