Adaptive Total Variation Image Deconvolution: Application to Magnetic Resonance Imaging
نویسندگان
چکیده
This paper presents a new approach to image deconvolution (deblurring), under total variation (TV) regularization, which is adaptive in the sense that it doesn’t require the user to specify the value of the regularization parameter. We follow the Bayesian approach of integrating out this parameter, which is achieved by using an approximation of the partition function of the Bayesian interpretation of the TV regularizer. The resulting optimization problem is then attacked using a majorizationminimization algorithm. We present some results on sparse reconstruction, and show that the results obtain similar performance when compared with hand-tuned TV reconstruction.
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