Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
A Nonnegatively Constrained Convex Programming Method for Image Reconstruction
SIAM Journal on Scientific Computing
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
Iterative methods for the reconstruction of astronomical images with high dynamic range
Journal of Computational and Applied Mathematics - Special issue: Applied computational inverse problems
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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In this paper a quasi-Newton projection method for image deblurring is presented. The image restoration problem is mathematically formulated as a nonnegatively constrained minimization problem where the objective function is the sum of the Kullback---Leibler divergence, used to express fidelity to the data in the presence of Poisson noise, and of a Tikhonov regularization term. The Hessian of the objective function is approximated so that the Newton system can be efficiently solved by using Fast Fourier Transforms. The numerical results show the potential of the proposed method both in terms of relative error reduction and computational efficiency.