Sparse Wavelet Representations of Spatially Varying Blurring Operators
نویسندگان
چکیده
منابع مشابه
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain
Restoration of images degraded by spatially varying blurs is an issue of increasing importance in the context of photography, satellite or microscopy imaging. One of the main difficulty to solve this problem comes from the huge dimensions of the blur matrix. It prevents the use of naive approaches for performing matrix-vector multiplications. In this paper, we propose to approximate the blur op...
متن کاملLearning Sparse Wavelet Representations
In this work we propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained using gradient descent. We show that the model is capable of learning structured wavelet filters from synthetic and real data. The learned w...
متن کاملAdaptive transforms for image coding using spatially varying wavelet packets
We introduce a novel, adaptive image representation using spatially varying wavelet packets (WPs), Our adaptive representation uses the fast double-tree algorithm introduced previously (Herley et al., 1993) to optimize an operational rate-distortion (R-D) cost function, as is appropriate for the lossy image compression framework. This involves jointly determining which filter bank tree (WP freq...
متن کاملSpatially Varying Blur Recovery - Diagonal Approximations in the Wavelet Domain
Restoration of images degraded by spatially varying blurs is an issue of increasing importance. Many new optical systems allow to know the system point spread function at some random locations, by using microscopic luminescent structures. Given a set of impulse responses, we propose a fast and efficient algorithm to reconstruct the blurring operator in the whole image domain. Our method consist...
متن کاملLearning sparse, overcomplete representations of time-varying natural images
I show how to adapt an overcomplete dictionary of spacetime functions so as to represent time-varying natural images with maximum sparsity. The basis functions are considered as part of a probabilistic model of image sequences, with a sparse prior imposed over the coefficients. Learning is accomplished by maximizing the log-likelihood of the model, using natural movies as training data. The bas...
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: SIAM Journal on Imaging Sciences
سال: 2015
ISSN: 1936-4954
DOI: 10.1137/151003465