Deconvolution and Denoising for Confocal Microscopy
DOI10.1007/978-3-642-31208-3_4zbMath1400.92308OpenAlexW23467371MaRDI QIDQ4554272
Gilbert Engler, Laure Blanc-Féraud, Praveen Pankajakshan, Josiane Zerubia
Publication date: 13 November 2018
Published in: Modeling in Computational Biology and Biomedicine (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-642-31208-3_4
blind deconvolutiontotal variation regularizationgraphical processing unitdeconvolution algorithmconfocal laser scanning microscope
Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Uses Software
Cites Work
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- Nonlinear total variation based noise removal algorithms
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Filtering and deconvolution by the wavelet transform
- Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation
- Nested Iterative Algorithms for Convex Constrained Image Recovery Problems
- An EM algorithm for wavelet-based image restoration
- Efficient gradient projection methods for edge-preserving removal of Poisson noise
- A Proximal Iteration for Deconvolving Poisson Noisy Images Using Sparse Representations
- Restoration of Poissonian Images Using Alternating Direction Optimization
- Least Squares Methods for Ill-Posed Problems with a Prescribed Bound
- Image Processing and Analysis
- Blind Image Deconvolution
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