Deconvolution under Poisson noise using exact data fidelity and synthesis or analysis sparsity priors
نویسندگان
چکیده
منابع مشابه
Confidence-constrained joint sparsity recovery under the Poisson noise model
Our work is focused on the joint sparsity recovery problem where the common sparsity pattern is corrupted by Poisson noise. We formulate the confidence-constrained optimization problem in both least squares (LS) and maximum likelihood (ML) frameworks and study the conditions for perfect reconstruction of the original row sparsity and row sparsity pattern. However, the confidence-constrained opt...
متن کاملPoisson noise reduction in deconvolution microscopy
Computational optical sectioning microscopy is a powerful tool to reconstruct three-dimensional images from optical two-dimensional sections of a biological specimen acquired by means of a fluorescence microscope. Due to limiting factors in the imaging systems, the images are degraded by both the optical system and detection process. Each of the two-dimensional section of the three-dimensional ...
متن کاملDeconvolution using natural image priors
If the matrix C f is a full rank matrix, and no noise is involved in the imaging process, the simplest approach to deconvolve y is to invert C f and define x = C −1 f y. Or in the frequency domain, X(ν,ω) = Y (ν,ω)/F(ν,ω) This, however, is very rarely stable enough. For example, the inverse is not defined in frequencies (ν,ω) for which F(ν,ω) = 0. Even in case |F(ν,ω)| is not exactly 0 but smal...
متن کاملLearning ℓ1-based analysis and synthesis sparsity priors using bi-level optimization
We consider the analysis operator and synthesis dictionary learning problems based on the the `1 regularized sparse representation model. We reveal the internal relations between the `1-based analysis model and synthesis model. We then introduce an approach to learn both analysis operator and synthesis dictionary simultaneously by using a unified framework of bi-level optimization. Our aim is t...
متن کاملLearning $\ell_1$-based analysis and synthesis sparsity priors using bi-level optimization
We consider the analysis operator and synthesis dictionary learning problems based on the the `1 regularized sparse representation model. We reveal the internal relations between the `1-based analysis model and synthesis model. We then introduce an approach to learn both analysis operator and synthesis dictionary simultaneously by using a unified framework of bi-level optimization. Our aim is t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistical Methodology
سال: 2012
ISSN: 1572-3127
DOI: 10.1016/j.stamet.2011.04.008