Abstract

Images obtained by photon-counting sensors are always contaminated with Poisson noise. Total variation (TV) has been extensively researched in image deconvolution because of its remarkable ability to preserve details. However, TV is based on the requirement that the global image gradient obeys a Laplacian distribution and can hardly maintain the information of each part of the image. We extended the global TV to nonlocal modeling and established an intensity-adaptive nonlocal regularization based on similar blocks. Meanwhile, to restrain the staircase effect caused by first-order regularization, we proposed a new hybrid nonlocal regularization by modeling the sparsity of the high-order derivative. An efficient alternating direction method of multipliers algorithm was employed to solve the proposed model, and the adaptive selection strategy of regularization parameters in the model was further studied and analyzed. The experimental results show that the proposed hybrid high-order nonlocal gradient sparsity regularization model achieves a substantial computational time improvement compared to another nonlocal restoration algorithm while producing a relatively clear recovery image.

© 2018 Optical Society of America

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