Restoration of remote sensing images based on nonconvex constrained high-order total variation regularization

Abstract

Convex total variation (TV) regularization models have been widely used in remote sensing image restoration problems; however, these models tend to produce staircase effects. We consider a nonconvex second-order TV regularization model with linear constraints for remote sensing image restoration. To solve the nonconvex second-order TV regularization model, we propose an efficient alternating minimization algorithm based on generalized iterated shrinkage algorithm and alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed model, which can reduce staircase effects while preserving edges. In terms of signal-to-noise ratio and structural similarity index measure, the experimental results show that our proposed model and algorithm can give better performance compared with some state-of-the-art methods.

Constrained Minimization Problem for Image Restoration Based on Non-Convex Hybrid Regularization

“…The accelerated alternating minimization method is proposed on the basis of the well-known variable-splitting and penalty technique. Next, we need to introduce three auxiliary variables ω, v, r, and transform the optimization problem (5) into the following constrained problem:…”

“…The paper is organized as follows. In Section 2, we use the proposed alternate minimization algorithm to solve the proposed model (5). In Section 3, we present numerical results and performance comparisons.…”

A new smoothing method for solving nonlinear complementarity problems

“…It is obvious that when θ = , φ θ reduces to the famous CHKS smoothing function, θ = , φ θ reduces to the famous Fischer-Burmeister smoothing function. As we all know, these two smoothing functions and their variants have been widely used in designing smoothing-type methods for solving mathematical programming problems, such as the nonlinear complementarity problems (NCPs) [16][17][18][19][20][21][22], the second-order cone complementarity problems(SOCCPs) [23][24][25][26][27][28][29], the second-order cone programming (SOCP) [30][31][32][33][34][35][36][37][38][39].…”

Hybrid variational model based on alternating direction method for image restoration

“…Although the total variation regularization can preserve sharp edges very well, it also causes some staircase effects [31,32]. To overcome this kind of staircase effect, some highorder total variational models [33][34][35][36][37][38][39] and fractional-order total variation models [40][41][42][43][44] are introduced. It has been proved that the high-order TV norm can remove the staircase effect and preserve the edges well in the process of image restoration.…”