Speckle Noise Reduction via Nonconvex High Total Variation Approach

Abstract

We address the problem of speckle noise removal. The classical total variation is extensively used in this field to solve such problem, but this method suffers from the staircase-like artifacts and the loss of image details. In order to resolve these problems, a nonconvex total generalized variation (TGV) regularization is used to preserve both edges and details of the images. The TGV regularization which is able to remove the staircase effect has strong theoretical guarantee by means of its high order smooth feature. Our method combines the merits of both the TGV method and the nonconvex variational method and avoids their main drawbacks. Furthermore, we develop an efficient algorithm for solving the nonconvex TGV-based optimization problem. We experimentally demonstrate the excellent performance of the technique, both visually and quantitatively.

Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

“…So the proposed model can remove the large noise and preserve the structures of the restored image better through the parameters and . Thirdly, [20] alsostudied the high order variational model for multiplicative noise removal; however it requires the restored images to belong to the more complex function space (refers to [20]).…”

“…Using the constrained TV norm, Hao et al [13] put forward a dual method and its acceleration. For other methods for multiplicative noise removal refer to [14][15][16][17][18][19][20][21][22]. However, it is well known that the TV model suffers from the so-called staircase effect.…”

A New Variational Approach for Multiplicative Noise and Blur Removal

“…Multiplicative noise seriously interrupts with fundamental tasks, such as object recognition or target detection, image segmentation and classification. Due to the coherent nature of the image acquisition systems, in the multiplicative noise models, the noise field is multiplied by the observed image, which is described by a probability density functions (non-Gaussian), with Gamma and Rayleigh being common models [ 16 , 17 ].…”

“…For multiplicative Gamma noise reduction, a variational model involving Curvelet coefficients was introduced in [ 35 ]. Most recent works include, both additive and multiplicative noise removal model by Chumchob et al [ 36 ], a higher-order MRF based variational model for removing multiplicative noise presented by Chen et al [ 37 ], speckle removal via higher-order TV based approach introduced by Feng et al [ 16 ] and speckle noise removal via non-convex high total variation approach by Wu et al [ 17 ] and so on.…”

“…Here, the balance between the first and second order derivatives of u is provided by the definition in Eq (7) . According to [ 17 ], the multiplicative noise removal with TGV regularization both can results to piecewise polynomial smoothness and can preserve edges of the images. See [ 16 , 17 , 43 – 45 ] for more details on TGV.…”