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Motivated by the capability of ℓp-regularized algorithms and the close connection of total variation (TV) to the ℓ1 norm, a pth-power TV denoted as TVp is proposed with the condition 0 ≤ p ≤ 1. It's difficulty to handle the TVp-regularized problem for image deblurring on account of the nonconvex part in the objective function, then we tackle this problem by a weighted TV (WTV) minimization where the...
Images obtained from unconstrained environments may be blurred by unknown kernels and affected due to noise. This paper presents a new total variation minimization-based method for blindly deblurring such images. Unlike the alternating optimization-based algorithms, the proposed algorithm adopts a joint estimation strategy to estimate the unknown blurring kernel and the unknown image in an iterative...
A new fast monotonic blind deconvolution algorithmic method is investigated based on the constrained variational minimization framework under the periodic boundary conditions. The contributions of our methodology are that the blur operator identification and image restoration can be simultaneously optimized even under high noise level as compared to previous methods. Specifically, the monotone fast...
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