IEEE Trans Image Process - Adaptive dictionary learning in sparse gradient domain for image recovery.

Tópicos

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Resumo

Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically, we first train dictionaries from the horizontal and vertical gradients of the image and then reconstruct the desired image using the sparse representations of both derivatives. The proposed method enables local features in the gradient images to be captured effectively, and can be viewed as an adaptive extension of the TV regularization. The results of various experiments on MR images consistently demonstrate that the proposed algorithm efficiently recovers images and presents advantages over the current leading CS reconstruction approaches.

Resumo Limpo

imag recoveri undersampl data alway challeng due implicit illpos natur becom fascin emerg compress sens cs theori paper propos novel gradient base dictionari learn method imag recoveri effect integr popular total variat tv dictionari learn techniqu framework specif first train dictionari horizont vertic gradient imag reconstruct desir imag use spars represent deriv propos method enabl local featur gradient imag captur effect can view adapt extens tv regular result various experi mr imag consist demonstr propos algorithm effici recov imag present advantag current lead cs reconstruct approach

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