# Enhancing Matrix Completion Using a Modified Second-Order Total Variation.

1. IntroductionMatrix Completion (MC) refers to the problem of filling in missing entries of an unknown matrix from a limited sampling of its entries. This problem has received widespread attentions of researchers in many real applications; see, e.g., [1-3]. Generally, it is impossible to exactly recover arbitrary matrices from such a problem without some additional information since the problem is extraordinarily ill-posed [4] and hence has infinitely many solutions. However, in many realistic scenarios, very often the matrix we wish to recover is low-rank or can be approximated by a low-rank matrix, as illustrated in Figure 1. Therefore, an immediate idea to model MC is to pursue the low rank of matrices by solving the following optimization problem:

[mathematical expression not reproducible], (1)

where X, M [member of] [R.sup.mxn] and the entries of M in location set [OMEGA] [subset] [m] x [n] ([k] [??] {1, 2, ... , k}) are given while the remaining entries are missing.

Unfortunately, problem (1) is of little practical use since it is NP-hard in general. To circumvent this issue, a nuclear norm minimization method was suggested in [4, 5], which solves

[mathematical expression not reproducible], (2)

where [parallel]X[parallel]* [??] [[summation].sup.min{m,n}.sub.i=1] [[sigma].sub.i](X) and [[sigma].sub.i](X) denotes the ith largest singular value of X. Especially in [4], Candes and Recht have proved theoretically that any low-rank matrices can be exactly recovered by solving problem (2) under some necessary assumptions. Recently, some new and deep results on the above problem (2) have been obtained to tackle the theoretical defects in [4]; see, e.g., [6, 7]. Many algorithms have also been proposed to solve problem (2) and its variants, including the Accelerated Proximal Gradient (APG) algorithms [8, 9], the Iterative Reweighted Least Squares (IRLS) algorithm [10], and the Singular Value Thresholding (SVT) algorithms [11, 12].

Nuclear norm is the tightest convex approximation of rank function, but it is far from the closest one [13]. The relationship between nuclear norm and the rank function of matrices is similar to that between [l.sub.1] norm and [l.sub.0] norm of vectors [4, 14]. To get a much closer approximation to the rank function, many nonconvex functions used to approximate the [l.sub.0] norm, see, e.g., [15-19], have been successfully extended to replace the nuclear norm [20-22]. Two representative ones are the Schatten-q quasi-norm [20, 23, 24] and the truncated nuclear norm [21, 25] which is also called the partial sum of singular values [26]. The induced methods based on the above two approximate functions have been well investigated to deal with the MC problem [20, 21] and obtained much better performance than previous nuclear norm minimization method. Besides the above, there still exist many other methods used to tackle the MC problem; see, e.g., [27, 28] for the matrix factorization based methods and [29, 30] for the greedy methods.

Overall, almost all the existing MC methods are designed to approach the rank function and thus to induce the low rank. To some degree, low rank only characterizes the global prior of a matrix. In some instances, however, besides the low rank the matrices often have some additional structural priors. We still take the matrix (grey image) shown in Figure 1(a) for example. It is rich in smoothness features (priors). In other words, an entry and its neighboring entries in such a matrix often have small difference in their values. When dealing with such matrices, most existing MC methods in fact can not well capture their smoothness priors and hence may result in a poor performance. On the other hand, when the entries of a matrix reach an incredibly high missing ratio or the high-quality recovered matrices are in urgent need, one has no choice but to take their additional priors into consideration since it will become very difficult to exactly/robustly recover any matrices only by using their low rank prior. Therefore, how to mine more available priors of underlying matrices and integrate them into MC becomes a very crucial problem.

In this paper, we propose a new method that combines the low rank and smoothness priors of underlying matrices together to deal with the MC problem. Our method is not the first work on this topic, but by using a modified second-order total variation of matrices it becomes very competitive. In summary, the contributions of this paper are stated as follows:

(i) A modified second-order total variation and nuclear norm of matrix are combined to characterize the smoothness and low-rank priors of underling matrices, respectively, which makes our method much more competitive for MC problem.

(ii) An efficient algorithm is developed to deal with the optimization problem and the extensive experiments testify to the effectiveness of the proposed method over many state-of-the-art MC methods.

The remainder of this paper is organized as follows. In Section 2, we review some MC methods that simultaneously optimize both the low rank and smoothness priors of underlying matrices. Since matrices can be considered as the second-order tensors, this indicates that most tensor based completion methods can also be applied to the MC problem. Two related tensor completion methods are also included and introduced in this section. In Section 3, we present the proposed method and design an efficient algorithm to solve the induced optimization problem. Experimental results are presented in Section 4. Finally, the conclusion and future work are given in Section 5.

2. A Review on MC with Smoothness Priors

The low rank and smoothness priors of underlying matrices have been well studied in MC and visual data processing communities [31, 32], respectively. However, their combined work for MC is rarely reported. To the best of our knowledge, Han et al. [33] gave the first such work for MC and proposed a Linear Total Variation approximation regularized Nuclear Norm (LTVNN) minimization method, which takes the form

[mathematical expression not reproducible], (3)

where 0 [less than or equal to] [gamma] [less than or equal to] 1 is a penalty parameter. In LTVNN method, the smoothness priors of underlying matrices are constrained by a Linear Total Variation Approximation (LTVA), which is defined as

[parallel]X[[parallel].sub.LTVA] [??] [m.summation over (i=1)][n.summation over (i=1)][(([D.sup.v.sub.i,j] (X)).sup.2] + [([D.sup.h.sub.i,j] (X)).sup.2]), (4)

where

[mathematical expression not reproducible]. (5)

It has been shown in [33] that this LTVNN method is superior to many state-of-the-art MC methods, such as previous SVT method [11] and TNNR method [21], that only pursue the low rank. However, LTVNN method may result in staircase effect due to the use of linear total variation. On the other hand, the induced LTVNN algorithm does not solve problem (3) directly, but solves it by dividing problem (3) into two subproblems firstly, then solving these two subproblems independently and outputting the final results based on the solutions to these two subproblems lastly. Obviously, such an algorithm strategy is not consistent with their proposed optimization problem (3).

Recently, such consideration has been extended to deal with the tensor completion (TC) problem. From the perspective of tensor, vectors and matrices can be considered as the first- and second-order tensors, respectively. Therefore, most existing TC methods can still be used to deal with the MC problem. In 2014, Chen et al. [34] proposed a Tucker decomposition based Simultaneous Tensor Decomposition and Completion (STDC) method for TC. This STDC method solves the following optimization problem:

[mathematical expression not reproducible], (6)

where [mathematical expression not reproducible] is the Tucker decomposition of tensor X, [PHI] [??] (U(1) [cross product][U.sup.(2)] [cross product] ... [cross product][U.sup.(N)]) is a unified factor matrix that contains all factor matrices, [cross product] is the Kronecker product, and L is a preset matrix designed by some priors. Similar to the matrix case, the elements of tensor M in location set [OMEGA]([subset] [[I.sub.1]]x[[I.sub.2]]x ... x[[I.sub.N]]) are given while the rest are missing. In problem (6), [[summation].sup.N.sub.i=1] [[alpha].sub.i][parallel][U.sup.(i)][[parallel].sub.*], i.e., the first term of the objective function, characterizes the low rank of tensor X, and its second term enforces the similarity between individual components, which provides some smoothed results for X. Thus, STDC can be considered as a tensor extension of low-rank and smooth matrix completion. Later, based on another PARAFAC decomposition of tensors, Yokota et al. [35] proposed a Smooth PARAFAC tensor Completion (SPC) method that integrates low rank and smoothness priors of tensor by solving

[mathematical expression not reproducible], (7)

where Z [??] [[summation].sup.R.sub.r=1] [g.sub.r] x [u.sup.(1).sub.r] [dot encircle] [u.sup.(2).sub.r] [dot encircle] ... [dot encircle] [u.sup.(N).sub.r] is the PARAFAC decomposition of tensor Z, [dot encircle] is the outer product, [[OMEGA].sup.c] stands for the complement set of [OMEGA], [parallel]*[[parallel].sub.p] is the classical [l.sub.p] norm on vectors, and the matrix [mathematical expression not reproducible] is a smoothness constraint matrix, defined as

[mathematical expression not reproducible]. (8)

SPC method integrates low rank and smoothness priors into a single term (i.e., the second term of the objective function in problem (7)), and it is totally different from the STDC method. Besides, it has been proved numerically in [35] that SPC method performs much better than the SDTC method. More discussion on tensor and TC problems is beyond the scope of this paper, we refer interested readers to [35, 36] and the references within.

3. Proposed New Method

In this section, we propose a new method that combines the low rank and smoothness priors of underlying matrices to deal with the MC problem. In our method, the nuclear norm is used to characterize the low rank of underlying matrices while their smoothness priors are characterized by a modified second-order total variation (MSTV), which is defined as

[parallel]X[[parallel].sub.MSTV] [??] [m.summation over (i=1)][n.summation over (j=1)][(([R.sup.v.sub.i,j] (X)).sup.2] + [([R.sup.h.sub.i,j] (X)).sup.2]), (9)

where

[mathematical expression not reproducible]. (10)

and

[mathematical expression not reproducible]. (11)

Obviously, such a modified second-order total variation is a new metric for smoothness priors of matrices, which is not different from the classical second-order total variation [37], or from previous linear total variation approximation. However, from a functional perspective, it not only inherits the geometrical structure of second-order total variation which avoids the staircase effect caused by linear total variation approximation, but also yields a smooth function that allows more efficient numerical algorithms. Figure 2 shows the geometrical differences between the linear total variation approximation and our modified second-order total variation. Besides, equation (9) can be further written in matrix form as

[[parallel]X[parallel].sub.MSTV] = [[parallel][X.sup.T][R.sub.m][parallel].sup.2.sub.F] + [[parallel]X[R.sub.n][parallel].sup.2.sub.F], (12)

where for v = m, n

[mathematical expression not reproducible]. (13)

Now, the proposed methods that are based on nuclear norm and a modified second-order total variation can be modeled as the following optimization problem:

[mathematical expression not reproducible], (14)

where [lambda] > 0 is a penalty parameter. Although in problem (14) nuclear norm is used to characterize the low rank of matrices, it can be replaced by some other approximate functions of rank function, such as Schatten-q quasi-norm and truncated nuclear norm. On the other hand, both problem (14) and the aforementioned problem (3) share some similarities, but one will see later in the experimental part that the induced algorithm by proposed problem (14) performs much better than that of problem (3).

To solve problem(14), we adopt the Alternating Direction Method of Multipliers (ADMM) [38], which has been widely used to solve many application problems. To use ADMM, we first rewrite problem (14) as

[mathematical expression not reproducible]. (15)

Then the corresponding augmented Lagrangian function of (15) is presented as

[mathematical expression not reproducible], (16)

where Z [member of] [R.sup.mxn] is the dual variable and [rho] > 0 is a penalty parameter associated with the augmentation. According to ADMM, the iterative procedure can be described as follows:

(1) Fixing [Y.sup.(k)] and [Z.sup.(k)], solve for [X.sup.(k+1)] by

[mathematical expression not reproducible], (17)

which is equivalent to

[mathematical expression not reproducible], (18)

where G = [Y.sup.(k)] - [Z.sup.(k)]/[rho]. According to [11], problem (18) has a closed-form solution, which can be obtained by the singular value thresholding operator [D.sup.[rho]](*), i.e.,

[mathematical expression not reproducible], (19)

where G [??] [S.sub.G] * Diag([{[[sigma].sub.i](G)}.sub.1[less than or equal to]i[less than or equal to]m]) * [D.sup.T.sub.G] is the singular value decomposition of G.

(2) Fixing [X.sup.(k+1)] and [Z.sup.(k)], solve for [Y.sup.(k+1)] by

[mathematical expression not reproducible], (20)

which is equivalent to

[mathematical expression not reproducible], (21)

where H [??] [X.sup.(k+1)] + [Z.sup.(k)]/[rho]. By setting the derivative of J(Y) with respect to Y to zero, we have

2[lambda][R.sub.m][R.sup.T.sub.m]Y + Y(2[lambda][R.sub.n][R.sup.T.sub.n] + [rho][I.sub.n]) = [rho]H, (22)

where [I.sub.n] is an n x n identity matrix. Equation (22) is the well known Sylvester equation [39]. In this paper, we resort to the Matlab command lyap to solve it; i.e.,

[??] = lyap (2[lambda][R.sub.m][R.sup.T.sub.m],2[lambda][R.sub.n][R.sup.T.sub.n] + [rho][I.sub.n], - [rho]H). (23)

Algorithm 1: Solving problem (14) via ADMM. Input: [lambda], [OMEGA], [M.sub.[OMEGA]], [X.sup.(0)] = [Y.sup.(0)] = [M.sub.[OMEGA]], [Z.sup.(0)] = 0, [rho], [R.sub.m], and [R.sub.n] Output: [X.sup.#] 1: Initialize k [left arrow] 1 2: repeat 3: Update [X.sup.(k+1)] by (19); 4: Update [??] by (23); 5: Update [Y.sup.(k+1)] by (24); 6: Update [Z.sup.(k+1)] by (25); 7: Update [rho] [left arrow] min{t[rho], [[rho].sub.max]}; 8: Update k [left arrow] k + 1. 9: until a certain stopping criterion is satisfied. 10: return [mathematical expression not reproducible].

After we obtain [??], i.e., the solution to (22), we can hence approximately obtain [Y.sup.(k+1)] by

[mathematical expression not reproducible]. (24)

(3) Update Z(k+1) by

[Z.sup.(k+1)] = [Z.sup.(k)] + [rho]([X.sup.(k+1)] - Y(k+1)). (25)

We summarize the iterative procedure in Algorithm 1.

Note that we do not use a fixed [rho] but adopt an adaptive updating strategy for [rho] (see the 7th step in Algorithm 1). Such a setting is mainly inspired by the recent studies on ADMM [21, 26, 40]. In fact, both the theoretical and applied results have shown that such a setting can help achieve quick convergence for ADMM based algorithms and hence avoid high computation cost. Without loss of generality, we simply set t = 1.05 and [[rho].sub.max] = [10.sup.3] for Algorithm 1. It should also be noted that the performance of the proposed algorithm can be further improved if one can optimize these two parameters.

4. Numerical Experiments

In this section, some experiments are conducted to evaluate the performance of the proposed method. They mainly involve solving the randomly masked image completion problem and the text masked image reconstruction problem. The former problem aims to complete the incomplete images generated by masking their entries randomly, and the latter one aims to reconstruct the images with text. In our experiments, the Lenna image shown in Figure 1(a) and 20 widely used test images shown in Figure 3 are used to generate the desired test matrices. To evaluate the quality of the reconstructed images, Structural SIMilarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indices are adopted. We refer readers to [41] for SSIM index. As to PSNR index, suppose Mean Squared Error (MSE) is defined as [mathematical expression not reproducible], where [absolute value of [[OMEGA].sup.c]] counts the number of elements in [[OMEGA].sup.c] and M and [X.sup.#] are the original image and the reconstructed image, respectively; then we can calculate the PSNR value by 10 [log.sub.10] ([255.sup.2]/MSE). The missing ratio of an incomplete image is always defined as [absolute value of [[OMEGA].sup.c]]/(m x n). All the experiments are implemented in Matlab and the codes can be downloaded from https://github.com/DongSylan/LIMC.

4.1. Convergence Behavior. In this part, we conduct some experiments to examine the convergence behavior of our Algorithm 1. The desired test matrices are generated by masking the entries in Lenna image randomly with a 60% missing ratio. Before the convergence analysis, we have to determine a proper penalty parameter [lambda] since it plays a vital role in our Algorithm 1. Figure 4 plots the relationship between [lambda] and the PSNR and SSIM results obtained by our Algorithm 1. It is easy to see that [lambda] = [10.sup.-4] is the best selection. In above experiments, we initialize [rho] = [10.sup.-2] and set the stopping criterion as

n (k) [??] [[parallel][X.sup.(k+1)] -[X.sup.(k)][[parallel].sub.F]/ max {[parallel][X.sub.(k)][[parallel].sub.F], 1}] < [10.sup.-4] or k > 500, (26)

where, here and throughout, n(k) is called the Relative Neighboring Iteration Error (RNIE). Similar to RNIE, we define Relative True Iteration Error (RTIE) as t(k) [??] [parallel][X.sup.(k+1)]- M[parallel]F/[[parallel]M[parallel].sub.F]. In what follows, we always set [lambda] = [10.sup.-4] and [rho] = [10.sup.-2] for our Algorithm 1.

Figure 5 plots the convergence results of our Algorithm 1. One can easily see that both the RNIE and RTIE decrease rapidly and consistently. Specifically, in terms of RNIE, when k exceeds 200 it tends to be smaller than [10.sup.-4]. However, under the same condition, RTIE tends to be a constant. These observations to some degree indicate that the stopping criterion (26) is reasonable for our Algorithm 1.

4.2. Randomly Masked Image Completion. To illustrate the effectiveness of our method, we compare our Algorithm 1 with 5 other state-of-the-art algorithms. They are IRLS algorithm [24], PSSV algorithm [26], LTVNN algorithm [33], and the SPC algorithms [35] with TV and QV smoothing, i.e., the SPC-TV and SPC-QV algorithms. In our experiments, we set q = 0.5, [lambda] = [10.sup.-4], and K = 20 for IRLS algorithm, [gamma] = [10.sup.-3] for PSSV algorithm, [gamma] = 0.5 and [lambda] = 1 for LTVNN algorithm, [rho] = [0.01, 0.01] for SPC-TV algorithm, and [rho] = [0.5, 0.5] for SPC-QV algorithm. We also uniformly set their maximum iterations to 500.

We start with completing the Lenna image masked randomly with several different missing ratios [member of] {70, 65, 60, 55, 50%}.The obtained PSNR and SSIM results are presented in Table 1, where the highest PSNR and SSIM values are underlined and marked in bold, while their second highest values are underlined only. One can directly see that, when the missing ratio decreases or in other words the known information increases, all the algorithms tend to perform better. However, our Algorithm 1 performs better than the rest of the algorithms in both PSNR and SSIM perspectives whenever the missing ratio ranges. Specifically, when compared to the IRLS and PSSV algorithms that only pursue the low rank of underlying matrices, our Algorithm 1 improves their PSNR and SSIM performance by more than 5dB and 0.2, respectively. Figure 6 shows the reconstructed images obtained by these algorithms. It is easy to find that our Algorithm 1 not only avoids the staircase effect resulting from LTVNN algorithm, but also leads to a better smoothness performance than SPC-TV and SPC-QV algorithms.

To further confirm the effectiveness of our method, we apply our Algorithm 1, together with other 5 algorithms, to 20 widely used test images (Figure 3). The obtained results are presented in Table 2. An overall impression observed from Table 2 is that our Algorithm 1 achieves the highest SSIM in all cases and the highest PSNR in almost all cases. Although in terms of PSNR our Algorithm 1 performs weaker than SPC-QV algorithm in some cases, but these cases only occupy a small proportion (13/80) of all the cases, and the PSNR difference in these cases is also very small (less than 0.8dB).

4.3. Text Masked Image Reconstruction. Compared to the randomly masked image completion problem, text masked image reconstruction is a relatively hard task since the entries masked by the text are not randomly distributed in the matrix and the text may mask some important texture information. However, this problem can still be transformed into an MC problem by checking the position of the text first and regarding the corresponding entries as missing values.

Figure 7 shows the visual results obtained by abovementioned algorithms on reconstruction of the Lenna image with text. In terms of PSNR and SSIM values, our Algorithm 1 and the SPC-QV algorithm rank first and second, respectively, followed by the rest of the algorithms. In terms of visual quality, the proposed Algorithm 1 well reconstructs the original image and its local feature structure is also well kept without seeing any text traces. In contrast, the images reconstructed by IRLS and PSSV algorithms are covered with the obvious text traces. The LTVNN algorithm suffers from staircase effect again. The image reconstructed by the SPC-QV algorithm is better than that by SPC-TV algorithm, but still has some faint text traces. Based on previous 20 test images, we generate another 20 desired images with text (Figure 8) to compare our Algorithm 1 with 5 other algorithms in dealing with the text masked image reconstruction problem. The obtained PSNR and SSIM results are reported in Table 3, which confirms again the excellent performance of our Algorithm 1.

5. Conclusion and Future Work

This paper proposed a new method that combines the low rank and smoothness priors of matrices to tackle the matrix completion problem. Different from previous LTVNN method, our proposed method characterizes the smoothness of matrices by using a modified second-order total variation, which not only avoids the staircase effect caused by LTVNN method, but also leads to a better performance. Even compared to the recently emerged smooth PARAFAC tensor completion method, our method is still highly competitive in both PSNR and SSIM perspectives. The extensive experiments further confirm the excellent performance of our proposed method. Potential future work includes replacing the nuclear norm used in our method with other (nonconvex) low-rank promoting functions, integrating more available priors of matrices to enhance the performance of existing matrix completion methods, and applying our modified second-order total variation to the tensor completion problems.

https://doi.org/10.1155/2018/2598160

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This work was supported by the Natural Science Foundation of China under Grants Nos. 61273020 and 61673015.

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Wendong Wang (iD) and Jianjun Wang (iD)

School of Mathematics and Statistics, Southwest University, Chongqing 400715, China

Correspondence should be addressed to Jianjun Wang; wjj@swu.edu.cn

Received 17 March 2018; Revised 20 July 2018; Accepted 14 August 2018; Published 12 September 2018

Academic Editor: Seenith Sivasundaram

Caption: Figure 1: (a) Lenna image (256 x 256). (b) All the singular values of Lenna image are sorted from larger to smaller. A simple calculation will lead to the fact that the top 40 singular values contribute more than 80% information of all singular values.

Caption: Figure 2: The black points denote the local but nonedged entries, the red points denote the corresponding neighboring entries concerned in LTVA and our MSTV, and the grey points denote the remaining entries of matrices. Obviously, our MSTV takes more informative neighboring entries into consideration and hence yields a better performance.

Caption: Figure 3: The test images. All are 256 x 256 and will be numbered in order from 1 to 20, from left to right, and from top to bottom.

Caption: Figure 4: Penalty parameter [lambda] selection.

Caption: Figure 5: Convergence behavior of Algorithm 1.

Caption: Figure 6: Visual results on Lenna image with 60% missing ratio by different algorithms. (a) Original image. (b) Randomly masked image. (c) IRLS: PSNR=22.91dB, SSIM=0.707. (d) PSSV: PSNR=23.21dB, SSIM=0.696. (e) LTVNN: PSNR=24.91dB, SSIM=0.853. (f) SPC-TV: PSNR=25.24dB, SSIM=0.801. (g) SPC-QV: PSNR=27.50dB, SSIM=0.898. (h) Ours: PSNR=28.96dB, SSIM=0.936. The figure is better viewed in zoomed PDF.

Caption: Figure 7: Visual results on Lenna image with text by different algorithms. (a) Original image. (b) Text masked image. (c) IRLS: PSNR=22.14dB, SSIM=0.903. (d) PSSV: PSNR=24.41dB, SSIM=0.929. (e) LTVNN: PSNR=25.33dB, SSIM=0.962. (f) SPC-TV: PSNR=25.33dB, SSIM=0.941. (g) SPC-QV: PSNR=27.07dB, SSIM=0.966. (h) Ours: PSNR=29.12dB, SSIM=0.984.The figure is better viewed in zoomed PDF.

Caption: Figure 8: The desired text masked images. They are numbered in the same manner as we stated in Figure 3.

Table 1: PSNR|SSIM results on Lenna image with different missing ratios by different algorithms. 70% 65% 60% 55% IRLS 21.89|0.595 22.49|0.661 22.91|0.707 23.13|0.747 PSSV 21.80|0.585 22.60|0.646 23.21|0.696 23.90|0.744 LTVNN 23.89|0.793 24.42|0.824 24.91|0.853 25.41|0.881 SPC-TV 24.29|0.743 24.88|0.776 25.24|0.801 25.72|0.829 SPC-QV 26.66|0.864 27.15|0.883 27.50|0.898 27.89|0.912 Ours 27.87|0.906 28.46|0.921 28.96|0.936 29.54|0.948 50% IRLS 23.34|0.774 PSSV 24.61|0.791 LTVNN 25.90|0.900 SPC-TV 26.06|0.852 SPC-QV 28.18|0.925 Ours 29.89|0.957 Table 2: PSNR|SSIM results on 20 test images with different missing ratios by different algorithms. Missing ratio=70% IRLS PSSV LTVNN SPC-TV 1 27.58|0.802 27.88|0.821 29.32|0.902 29.14|0.864 2 21.34|0.586 21.43|0.593 23.37|0.768 23.50|0.715 3 22.03|0.596 22.12|0.606 24.29|0.804 24.70|0.761 4 19.87|0.470 20.19|0.487 21.95|0.645 21.55|0.592 5 22.77|0.630 23.25|0.644 24.67|0.755 24.71|0.735 6 22.72|0.623 22.56|0.609 24.55|0.766 24.86|0.742 7 20.76|0.582 20.91|0.592 22.64|0.804 23.11|0.734 8 24.26|0.683 24.14|0.689 25.48|0.839 26.86|0.808 9 20.40|0.558 20.33|0.557 22.01|0.687 22.14|0.667 10 23.39|0.621 23.25|0.614 25.09|0.730 25.34|0.731 11 19.19|0.539 19.60|0.571 21.69|0.798 21.53|0.690 12 17.87|0.525 17.92|0.528 19.80|0.696 19.95|0.658 13 24.69|0.619 24.64|0.616 27.07|0.783 26.83|0.742 14 24.00|0.668 23.89|0.666 25.53|0.777 25.94|0.766 15 23.44|0.595 23.71|0.605 25.64|0.735 25.38|0.707 16 19.73|0.545 19.72|0.546 21.43|0.750 21.92|0.690 17 19.76|0.514 19.59|0.504 21.94|0.704 21.96|0.653 18 20.12|0.555 19.96|0.543 22.11|0.748 22.67|0.708 19 21.83|0.604 21.72|0.599 23.63|0.809 24.35|0.758 20 20.27|0.577 20.38|0.584 22.01|0.748 22.21|0.703 Missing ratio=55% IRLS PSSV LTVNN SPC-TV 1 29.45|0.887 29.98|0.905 30.87|0.946 30.06|0.899 2 22.91|0.750 23.21|0.745 24.53|0.859 24.75|0.815 3 23.36|0.743 24.58|0.780 25.88|0.890 26.20|0.850 4 21.15|0.658 21.03|0.643 22.65|0.769 22.18|0.718 5 24.17|0.766 24.61|0.783 25.71|0.851 25.68|0.830 6 23.95|0.758 24.54|0.763 25.94|0.859 26.05|0.829 7 22.31|0.743 22.98|0.752 24.04|0.889 24.55|0.824 8 25.81|0.810 27.13|0.830 27.11|0.906 28.58|0.875 9 21.67|0.715 21.77|0.714 23.05|0.803 23.20|0.783 10 24.47|0.750 25.07|0.772 26.38|0.836 26.62|0.832 11 21.00|0.710 21.15|0.702 22.89|0.878 22.72|0.781 12 19.09|0.683 19.66|0.705 20.97|0.814 21.27|0.782 13 25.94|0.764 26.25|0.760 28.17|0.865 27.83|0.830 14 25.35|0.790 25.92|0.807 26.93|0.868 27.41|0.857 15 24.75|0.744 24.91|0.746 26.54|0.831 26.24|0.806 16 21.12|0.706 21.64|0.707 22.77|0.853 23.24|0.795 17 20.94|0.670 21.51|0.684 23.20|0.821 23.19|0.770 18 21.24|0.696 22.64|0.733 23.96|0.856 24.39|0.816 19 23.00|0.750 24.39|0.770 25.43|0.898 26.09|0.848 20 21.64|0.734 22.13|0.740 23.20|0.848 23.60|0.813 Missing ratio=70% Missing ratio=60% SPC-QV Ours IRLS PSSV 1 30.59|0.910 31.96|0.936 29.11|0.869 29.35|0.882 2 25.29|0.819 25.25|0.838 22.61|0.712 22.67|0.700 3 27.37|0.879 28.73|0.922 23.12|0.709 23.80|0.730 4 22.64|0.692 22.60|0.728 20.90|0.610 20.79|0.596 5 26.15|0.816 26.04|0.829 23.98|0.735 24.22|0.742 6 26.94|0.838 27.13|0.861 23.69|0.721 23.94|0.716 7 24.94|0.852 25.24|0.887 21.98|0.704 22.21|0.702 8 28.62|0.879 27.85|0.882 25.56|0.782 26.21|0.788 9 23.78|0.762 23.68|0.784 21.37|0.674 21.28|0.665 10 27.25|0.816 27.54|0.838 24.26|0.717 24.48|0.725 11 23.23|0.810 23.29|0.850 20.69|0.669 20.68|0.662 12 22.04|0.780 22.41|0.800 18.89|0.642 19.12|0.654 13 28.70|0.833 29.22|0.864 25.69|0.731 25.73|0.719 14 27.96|0.847 27.92|0.861 25.12|0.760 25.25|0.765 15 26.92|0.791 27.11|0.814 24.48|0.704 24.49|0.700 16 24.06|0.825 24.54|0.868 20.85|0.666 21.04|0.658 17 24.10|0.787 24.74|0.835 20.76|0.634 20.94|0.630 18 25.71|0.846 27.10|0.898 20.98|0.659 21.76|0.675 19 26.93|0.889 27.76|0.927 22.78|0.715 23.64|0.721 20 23.84|0.806 23.20|0.812 21.41|0.695 21.63|0.693 Missing ratio=55% Missing ratio=50% SPC-QV Ours IRLS PSSV 1 31.04|0.930 34.03|0.973 29.90|0.901 30.74|0.923 2 26.44|0.887 26.65|0.906 23.15|0.782 23.80|0.787 3 28.66|0.924 30.47|0.958 23.59|0.772 25.42|0.824 4 23.34|0.796 23.44|0.826 21.28|0.696 21.26|0.687 5 27.06|0.883 27.30|0.902 24.36|0.793 25.02|0.819 6 27.90|0.892 28.12|0.909 24.08|0.783 25.01|0.799 7 26.21|0.905 27.00|0.944 22.37|0.772 23.48|0.790 8 30.04|0.918 29.39|0.931 26.04|0.831 28.08|0.861 9 24.77|0.849 24.93|0.871 21.84|0.749 22.19|0.757 10 28.45|0.888 29.01|0.906 24.62|0.778 25.66|0.812 11 24.29|0.872 24.42|0.911 21.17|0.743 21.60|0.742 12 23.44|0.868 23.97|0.887 19.31|0.721 20.38|0.758 13 29.58|0.891 30.55|0.920 26.11|0.792 26.70|0.796 14 29.19|0.906 29.52|0.922 25.52|0.813 26.65|0.843 15 27.85|0.867 28.52|0.893 24.94|0.773 25.25|0.781 16 25.29|0.891 26.10|0.928 21.28|0.738 22.22|0.752 17 25.20|0.865 26.08|0.903 21.09|0.706 22.00|0.732 18 27.18|0.905 28.91|0.943 21.36|0.727 23.50|0.783 19 28.44|0.931 29.61|0.962 23.19|0.777 25.20|0.815 20 25.23|0.883 24.58|0.890 21.77|0.764 22.67|0.783 Missing ratio=60% LTVNN SPC-TV SPC-QV Ours 1 30.41|0.935 29.84|0.888 30.94|0.924 33.55|0.965 2 24.22|0.833 24.34|0.784 26.10|0.868 26.23|0.888 3 25.44|0.868 25.78|0.825 28.27|0.911 29.96|0.949 4 22.49|0.736 22.01|0.681 23.19|0.768 23.21|0.800 5 25.44|0.824 25.41|0.803 26.80|0.864 27.00|0.882 6 25.53|0.833 25.69|0.802 27.61|0.875 27.85|0.895 7 23.57|0.865 24.04|0.797 25.79|0.890 26.45|0.929 8 26.68|0.886 28.11|0.852 29.81|0.906 29.14|0.917 9 22.73|0.769 22.85|0.748 24.45|0.824 24.55|0.846 10 25.96|0.805 26.19|0.801 28.08|0.867 28.61|0.888 11 22.52|0.856 22.36|0.753 23.96|0.853 24.07|0.894 12 20.66|0.784 20.89|0.746 23.07|0.845 23.57|0.865 13 27.81|0.843 27.51|0.805 29.26|0.875 30.11|0.906 14 26.49|0.842 26.96|0.830 28.83|0.889 29.03|0.905 15 26.22|0.800 25.93|0.773 27.50|0.842 28.04|0.870 16 22.41|0.826 22.81|0.764 24.93|0.872 25.67|0.913 17 22.86|0.789 22.89|0.738 24.96|0.844 25.75|0.885 18 23.39|0.827 23.84|0.783 26.75|0.889 28.43|0.932 19 24.95|0.877 25.59|0.822 28.02|0.920 29.13|0.953 20 22.89|0.823 23.19|0.779 24.85|0.861 24.22|0.868 Missing ratio=50% LTVNN SPC-TV SPC-QV Ours 1 31.47|0.957 30.46|0.910 31.45|0.937 34.76|0.978 2 24.88|0.881 25.15|0.842 26.84|0.905 27.10|0.922 3 26.35|0.910 26.69|0.873 29.04|0.936 30.94|0.965 4 22.82|0.800 22.36|0.753 23.56|0.823 23.68|0.851 5 25.95|0.873 25.93|0.854 27.31|0.900 27.61|0.918 6 26.26|0.880 26.37|0.852 28.12|0.905 28.33|0.920 7 24.28|0.906 24.78|0.848 26.40|0.917 27.36|0.954 8 27.62|0.922 29.14|0.892 30.49|0.927 29.80|0.942 9 23.36|0.833 23.55|0.815 25.09|0.872 25.25|0.890 10 26.71|0.860 27.02|0.858 28.81|0.905 29.45|0.922 11 23.09|0.896 22.92|0.806 24.46|0.887 24.65|0.923 12 21.39|0.846 21.82|0.818 23.99|0.892 24.56|0.909 13 28.40|0.884 28.08|0.851 29.80|0.906 30.82|0.932 14 27.26|0.887 27.73|0.876 29.49|0.919 29.90|0.934 15 26.77|0.855 26.52|0.832 28.12|0.887 28.86|0.911 16 23.16|0.878 23.66|0.825 25.69|0.908 26.56|0.941 17 23.52|0.848 23.55|0.804 25.51|0.885 26.44|0.918 18 24.40|0.880 24.88|0.844 27.60|0.920 29.49|0.953 19 25.88|0.917 26.53|0.871 28.67|0.941 29.94|0.968 20 23.49|0.872 23.93|0.840 25.58|0.900 25.00|0.909 Table 3: PSNR|SSIM results on reconstruction of 20 test images with text by different algorithms. IRLS PSSV LTVNN SPC-TV 1 28.56|0.956 30.47|0.975 30.91|0.981 29.78|0.965 2 22.13|0.911 23.96|0.930 24.43|0.955 24.62|0.939 3 22.27|0.895 25.45|0.944 25.41|0.963 25.78|0.948 4 21.14|0.897 21.68|0.904 22.69|0.933 22.50|0.920 5 23.60|0.919 25.18|0.943 25.33|0.951 25.40|0.945 6 22.85|0.902 24.94|0.932 25.74|0.956 25.56|0.941 7 21.14|0.906 23.30|0.933 23.20|0.959 23.70|0.937 8 24.93|0.926 29.08|0.962 27.33|0.969 28.72|0.956 9 21.12|0.905 22.70|0.926 22.94|0.940 23.38|0.935 10 23.84|0.912 26.49|0.948 26.04|0.949 26.76|0.950 11 20.20|0.895 21.32|0.911 22.21|0.958 22.07|0.924 12 18.12|0.892 20.51|0.926 20.64|0.943 20.97|0.933 13 25.32|0.920 27.09|0.938 27.95|0.959 27.72|0.947 14 24.31|0.920 26.74|0.951 26.67|0.958 27.18|0.953 15 24.30|0.913 25.41|0.929 26.37|0.948 26.06|0.938 16 20.19|0.890 22.56|0.920 22.41|0.949 22.97|0.931 17 19.76|0.874 21.80|0.908 22.77|0.942 22.61|0.925 18 19.77|0.872 23.90|0.933 23.72|0.953 23.97|0.937 19 21.43|0.895 24.76|0.931 25.19|0.967 25.12|0.943 20 20.69|0.902 22.71|0.934 22.80|0.951 23.02|0.941 SPC-QV Ours 1 30.74|0.973 34.17|0.992 2 26.13|0.962 26.65|0.972 3 27.70|0.971 30.19|0.987 4 23.43|0.939 23.58|0.950 5 26.62|0.960 26.96|0.969 6 27.30|0.964 27.98|0.973 7 25.00|0.963 26.48|0.983 8 30.12|0.971 31.02|0.979 9 24.66|0.953 25.07|0.962 10 28.19|0.964 29.34|0.974 11 23.41|0.951 23.72|0.971 12 22.62|0.956 23.61|0.967 13 29.28|0.965 30.72|0.977 14 28.58|0.968 29.21|0.977 15 27.42|0.955 28.50|0.968 16 24.80|0.961 26.33|0.977 17 24.27|0.953 25.57|0.969 18 26.48|0.966 29.24|0.984 19 27.25|0.971 29.55|0.988 20 24.44|0.961 24.18|0.967

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Title Annotation: | Research Article |
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Author: | Wang, Wendong; Wang, Jianjun |

Publication: | Discrete Dynamics in Nature and Society |

Date: | Jan 1, 2018 |

Words: | 8304 |

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