Noisy image magnification with total variation regularization and order-changed dictionary learning
- Jian Xu^{1, 2}Email author,
- Zhiguo Chang^{3},
- Jiulun Fan^{1},
- Xiaoqiang Zhao^{1},
- Xiaomin Wu^{1} and
- Yanzi Wang^{1}
https://doi.org/10.1186/s13634-015-0225-y
© Xu et al.; licensee Springer. 2015
Received: 7 August 2014
Accepted: 13 April 2015
Published: 6 May 2015
Abstract
Noisy low resolution (LR) images are always obtained in real applications, but many existing image magnification algorithms can not get good result from a noisy LR image. We propose a two-step image magnification algorithm to solve this problem. The proposed algorithm takes the advantages of both regularization-based method and learning-based method. The first step is based on total variation (TV) regularization and the second step is based on sparse representation. In the first step, we add a constraint on the TV regularization model to magnify the LR image and at the same time to suppress the noise in it. In the second step, we propose an order-changed dictionary training algorithm to train the dictionaries which is dominated by texture details. Experimental results demonstrate that the proposed algorithm performs better than many other algorithms when the noise is not serious. The proposed algorithm can also provide better visual quality on natural LR images.
Keywords
Image magnification Super-resolution Total variation regularization Dictionary training Sparse representation Image denoising1 Introduction
The technology of image magnification focuses on how to magnify a low resolution (LR) image and at the same time recover some high resolution (HR) details. The methods of this technology can be divided into three categories: the method based on up scaling [1], the method based on reconstruction [2-5], and the method based on learning [6]. Some methods based on up scaling, such as bilinear and bicubic interpolation (BI) [7], are popular since they have low computational complex, but they always produce blurring edges and suffer from artifacts since they use the invariant kernels for all kinds of local textures. Methods based on reconstruction aim at reconstructing the HR image by imitating the inverse process of degradation [2]. These methods rely on the rationality of the reconstructing model. The methods based on up scaling and reconstruction have smaller memory space costs than the learning-based methods in most of the cases. But it is difficult to use some simple mathematical models to fit the sophisticated natural conditions. This makes these methods can not recover many texture details. The learning-based methods are more flexible to deal with the problem [6]. They use the training images to learn the relationship between the HR and LR images, and many existing works have demonstrated their good effect for the high magnification factors.
There are two important aspects in the learning-based algorithms. The first is the feature extraction methods. The second is the learning models.
Many existing feature extraction methods can be utilized to extract features for image magnification problem. Gradient features [6,8], Gabor features [9], fields of experts (FoE) [10] features and histogram oriented gradients (HoG) [11] are developed. To deal with different texture features by different strategies, the input image can be separated into edge and texture components [12], shape and texture components [13,14], different texture regions [15], or different frequency bands [16,17].
The main idea of many existing learning-based models is to use some tools to learn the relationship between the LR and HR images. Neighbor embedding (NE) is based on the assumption that the LR and HR local patches have similar geometries in two distinct feature spaces [18]. However, finding neighbors in millions of data samples is a high time-exhaustive task for the NE-based algorithm. Canonical correlation analysis (CCA) [19-21] assumes that the corresponding HR and LR images have great inner product similarity after a transformation. Compared to the NE-based methods, CCA can accomplish the transformation with lower computational complexity. Sparse representation-based models are widely used [22] in image processing because of its good generalization ability. Yang et al. [6,8] proposed a classical model to transform the HR and LR images into a unified subspace. They suppose the HR and LR images should have the same sparse representations in the subspace. To accomplish the transformation, coupled dictionary training is an important step. Yang et al. proposed joint learning [6] and coupled learning [23] algorithms to train coupled dictionaries. The joint learning algorithm combines the LR and HR patch pairs together to convert the coupled dictionary training task into a single dictionary training task. However, the reliable sparse representations are not guaranteed to be found in the test phase. Yang’s coupled learning algorithm [23] uses the alternately steepest descent algorithm to update the LR and HR dictionaries. Zeyde et al. [8] use a single dictionary training algorithm to train the LR dictionary and then generate the HR dictionary by solving a least square problem. Xu et al. [24] alternately update the LR and HR dictionaries with K-singular value decomposition (K-SVD). In these dictionary training algorithms, Zeyde’s algorithm has the smallest time complexity. Since it is a too strict condition to let the LR and HR sparse representations to be exactly the same, some tools (such as the neural network [25] and linear transformation [26,27]) are employed to model the relationship between the two sparse representations. To accelerate the sparse representation-based algorithm, Timofte et al. group the dictionary atoms [28] or the training samples [29] to decrease the time complexity of calculating the sparse representations. Some algorithms can provide excellent results on some special image classes (such as face [30] and buildings [31]). Besides the abovementioned tools, support vector regression (SVR) [32], Kernel-based regression [33], deep convolutional neural network [34], and fuzzy rule-based prediction [35] are also used as the tools to solve the image magnification problem.
In real applications, the obtained LR images always contain noise (such as taking photos in low-light or strong interference conditions). Since some existing algorithm is not good at dealing with the noisy LR image, we propose an algorithm to cover the shortage. The destination of this algorithm is to reconstruct a clear HR image according to a noisy LR image. The proposed algorithm takes the advantage of both the regularization-based method and the learning-based method. We firstly use the regularization-based method to suppress the noise and then use the learning-based method to recover the details. To make it simple, we briefly call the proposed method total variation and order-changed dictionary training (TV-OCDT) algorithm.
- 1)
We propose a constraint for the total variation (TV) regularization-based image magnification model. The constraint is helpful to suppress the noise and recover sharp edges.
- 2)
We propose an order-changed dictionary training algorithm to train the coupled dictionaries. The traditional dictionary training algorithm firstly trains the LR dictionary. Then, generate the HR dictionary according to the LR dictionary. But we firstly train the HR dictionary and then generate the LR dictionary according to the HR dictionary. This strategy changes the dominated content of the dictionaries so that the texture details can be recovered well. Experimental results show that the proposed algorithm is superior to others on the noisy images.
The remainder of this paper is organized as follows. Section 2 describes the proposed algorithm. The experimental results are presented in Section 3. Section 4 concludes this paper.
2 The proposed algorithm
2.1 TV regularization with LR constraint
To fit the recovered HR image to the initial input LR image L ^{ s }, the famous iterative back projection (IBP) [38] algorithm is widely used in image magnification technology. It can be executed without storing any tools (such as data samples or dictionaries) and has low computational complexity.
where Z ^{ s,I B P } is the reconstructed HR image of IBP and ‘ F(·)’ is the operation of down sampling by BI.
where Z ^{ J } is the output of the Jth iteration and ‘ U(·)’ is the operation of up sampling by BI.
where Z ^{ s,T V } is the reconstructed HR image of TV regularization and λ is a Lagrange multipliers, ‘ G(·)’ is to calculate the gradients. But we found the traditional TV regularization is not effective enough to suppress the noise in image magnification (as shown in Figure 3).
where λ _{1} and λ _{2} are the Lagrange multipliers. Obviously, the motivation of the constraint is to make the LR image also have a small TV norm. This motivation is inspired by the classical Rudin-Osher-Fatemi (ROF) TV denoising model [40].
Inspired by the reference [39], the model (4) is solved with the following method.
where ‘ ∗’ is convolution.
where ‘ ∙/’ is to do division on the elements of matrices, ε is a positive parameter to avoid singularity and is set to 1 according to [39].
Figure 3 compares the images before and after TV regularization. As shown, the proposed TV regularization constraint is benefit to suppress the noise and reconstruct sharp step edges. More experimental results shown in Section 3 will further demonstrate the effect of the proposed TV model.
2.2 OCDT algorithm
The data set \(\left \{{\mathbf {p}}^{s,e}_{i}\right \}^{N}_{i=1}\) is generated by dividing Z ^{ s,e } into patches, where \({\mathbf {p}}^{s,e}_{i}\) and \({\mathbf {p}}^{s,d}_{i}\) are corresponding patches.
where α _{ i } is the sparse representation, D ^{ d } and D ^{ e } are dictionaries corresponding to \(\{{\mathbf {p}}^{s,d}_{i}\}^{N}_{i=1}\) and \(\{{\mathbf {p}}^{s,e}_{i}\}^{N}_{i=1}\), respectively, \({\mathbf {d}}^{d}_{r}\) and \({\mathbf {d}}^{e}_{r}\) are their rth dictionary atoms, \(\hat {T}\) is the sparseness constraint.
This model can be easily solved with Zeyde’s method [8]. But it is not proper to use the strategy to generate the D ^{ d } firstly and then calculate the D ^{ e } according to the D ^{ d }. We should change the order of the dictionary training. According to the observation, we found Z ^{ s,T V } is dominated by smooth regions and step edges, but lacks texture details. The smooth regions can be well recovered by BI, so the smooth training patches are dropped in dictionary training stage as Yang’s operation [23]. If we train the D ^{ d } firstly, D ^{ d } will be dominated by step edges. But we need coupled dictionaries dominated by texture details, since the step edges have been recovered by TV regularization in the previous steps. Z ^{ s,e } contains the lost texture details in Z ^{ s }. Therefore, we firstly train D ^{ e } and then calculate D ^{ d } according to D ^{ e }.
where \({\mathbf {P}}^{s,e}=\left [{\mathbf {p}}^{s,e}_{1}, {\mathbf {p}}^{s,e}_{2},\ldots, {\mathbf {p}}^{s,e}_{N}\right ]\).
where ς (set to 0.1) is a small positive parameter to avoid singularity, I is an identity matrix.
where \({\mathbf {P}}^{s,d}=\left [{\mathbf {p}}^{s,d}_{1}, {\mathbf {p}}^{s,d}_{2},\ldots, {\mathbf {p}}^{s,d}_{N}\right ]\), B=[β _{1},β _{2},⋯,β _{ n }].
2.3 Summary of the proposed algorithm
The dictionary training scheme of the TV-OCDT algorithm is summarized in Algorithm 1. Since it is difficult to estimate the noise level for a natural image in real applications, we trained the dictionaries with clear training images. The test images with different noise levels are all recovered by same dictionaries.
The reconstruction stage of the proposed algorithm is summarized in Algorithm 2.
3 Experiments
In this section, we will first introduce the experimental settings and compare our algorithm with five state-of-the-art algorithms. Then, we will discuss two influential factors for the sparse representation stage (including the patch size and the dictionary size) and two parameters in TV regularization stage (including two Lagrange parameters and iteration number). Finally, we will show the time complexity of the proposed algorithm.
3.1 Experimental settings
3.2 Comparison with other methods
The average PSNR and SSIM values for different methods (3 ×)
Method | Zeyde | ANR | ANR plus | SPM | DCN | Proposed |
---|---|---|---|---|---|---|
Without | 28.762 | 27.545 | 29.376 | 29.080 | 29.255 | 29.238 |
noise | 0.8539 | 0.8411 | 0.8535 | 0.8465 | 0.8453 | 0.8586 |
σ=5 | 27.044 | 26.851 | 27.181 | 27.089 | 27.191 | 27.673 |
0.7273 | 0.7129 | 0.7189 | 0.7268 | 0.7223 | 0.7980 | |
σ=10 | 24.460 | 24.101 | 24.020 | 23.714 | 23.996 | 24.836 |
0.5662 | 0.5440 | 0.5381 | 0.5280 | 0.5376 | 0.6381 | |
σ=15 | 22.129 | 21.698 | 21.420 | 20.912 | 21.281 | 22.079 |
0.4436 | 0.4216 | 0.4110 | 0.3898 | 0.4047 | 0.4801 | |
σ=20 | 20.238 | 19.762 | 19.403 | 18.811 | 19.190 | 19.838 |
0.3588 | 0.3383 | 0.3260 | 0.3029 | 0.3174 | 0.3716 |
The average PSNR and SSIM values for different methods (4 ×)
Method | Zeyde | ANR | ANR plus | SPM | DCN | Proposed |
---|---|---|---|---|---|---|
Without | 26.812 | 26.680 | 27.172 | 26.951 | 27.059 | 27.129 |
noise | 0.7841 | 0.7806 | 0.7860 | 0.7771 | 0.7780 | 0.7995 |
σ=5 | 25.494 | 25.335 | 25.623 | 25.535 | 25.766 | 26.055 |
0.6796 | 0.6651 | 0.6774 | 0.6809 | 0.6916 | 0.7409 | |
σ=10 | 23.476 | 23.138 | 23.115 | 22.863 | 23.337 | 23.762 |
0.5445 | 0.5208 | 0.5194 | 0.5100 | 0.5409 | 0.6061 | |
σ=15 | 21.485 | 21.055 | 20.858 | 20.428 | 20.964 | 21.413 |
0.4350 | 0.4100 | 0.4032 | 0.3831 | 0.4171 | 0.4709 | |
σ=20 | 19.810 | 19.323 | 19.045 | 18.503 | 19.005 | 19.428 |
0.3539 | 0.3298 | 0.3200 | 0.2982 | 0.3283 | 0.3697 |
3.3 Effects of the parameters
3.3.1 3.3.1 Effects of the dictionary size and patch size
The average PSNR and SSIM values for different patch and dictionary sizes (3 ×)
Patch size | Dictionary size | |||||
---|---|---|---|---|---|---|
256 | 512 | 1,024 | 256 | 512 | 1,024 | |
σ=5 | σ=10 | |||||
3 | 30.423 | 30.433 | 30.430 | 26.744 | 26.660 | 26.670 |
0.8869 | 0.8857 | 0.8873 | 0.7198 | 0.7197 | 0.7179 | |
5 | 30.422 | 29.883 | 30.437 | 26.660 | 27.567 | 26.572 |
0.8858 | 0.8552 | 0.8879 | 0.7148 | 0.7570 | 0.7113 | |
7 | 30.486 | 29.883 | 30.364 | 26.700 | 27.449 | 26.554 |
0.8896 | 0.8576 | 0.8840 | 0.7173 | 0.7495 | 0.7076 | |
σ=15 | σ=20 | |||||
3 | 22.928 | 22.823 | 22.856 | 19.379 | 19.356 | 19.325 |
0.4474 | 0.4432 | 0.4449 | 0.3381 | 0.3346 | 0.3395 | |
5 | 22.989 | 24.848 | 22.933 | 19.317 | 22.020 | 19.399 |
0.4523 | 0.5323 | 0.4513 | 0.3405 | 0.4493 | 0.3444 | |
7 | 22.853 | 24.881 | 22.821 | 19.357 | 22.013 | 19.237 |
0.4390 | 0.5373 | 0.4417 | 0.3350 | 0.4502 | 0.3347 |
The average PSNR and SSIM values for different patch and dictionary sizes (4 ×)
Patch size | Dictionary size | |||||
---|---|---|---|---|---|---|
256 | 512 | 1,024 | 256 | 512 | 1,024 | |
σ=5 | σ=10 | |||||
3 | 28.803 | 28.730 | 28.831 | 25.688 | 25.623 | 25.710 |
0.8333 | 0.8306 | 0.8354 | 0.6544 | 0.6575 | 0.6490 | |
5 | 28.799 | 28.769 | 28.877 | 25.637 | 25.704 | 25.607 |
0.8317 | 0.8289 | 0.8342 | 0.6498 | 0.6607 | 0.6505 | |
7 | 28.850 | 28.382 | 28.417 | 25.639 | 26.372 | 26.314 |
0.8328 | 0.8008 | 0.8031 | 0.6486 | 0.6780 | 0.6782 | |
σ=15 | σ=20 | |||||
3 | 22.076 | 22.027 | 21.945 | 19.302 | 19.217 | 19.366 |
0.3863 | 0.3892 | 0.3892 | 0.3472 | 0.3473 | 0.3455 | |
5 | 22.123 | 21.885 | 22.151 | 19.458 | 19.173 | 19.353 |
0.3971 | 0.3837 | 0.4024 | 0.3495 | 0.3413 | 0.3551 | |
7 | 21.962 | 23.776 | 23.777 | 19.212 | 21.650 | 21.870 |
0.3905 | 0.4734 | 0.4695 | 0.3397 | 0.4405 | 0.4628 |
For the 3 × magnification, all the best PSNR values are obtained when the dictionary size is 512. Most of the best SSIM values are also obtained when the dictionary size is 512. Therefore, we choose 512 as the dictionary size for the 3 × magnification. When the noise standard deviation is 5, the best patch size is 3. When the noise standard deviation is 10 and 20, the best patch size is 5. When the noise standard deviation is 15, the best patch size is 7. Therefore, there are no best patch size which is suitable for every noise standard deviation. But the worst values appear when the patch size is 7. Furthermore, larger patch size will result in larger time complexity. Therefore, we choose 5 in our experiments since it is suitable for two standard deviation values.
For the 4 × magnification, most of the best PSNR and SSIM values are obtained by the dictionary size 1,024. The patch size 7 gets the highest PSNR and SSIM values for most of the standard deviations. Therefore, we choose 1,024 as the best dictionary size and 7 as the best patch size.
3.3.2 3.3.2 Effects of the parameters in TV regularization
The average PSNR and SSIM values for different iteration numbers (3×)
Iteration numbers | σ | |||
---|---|---|---|---|
5 | 10 | 15 | 20 | |
100 | 30.589 | 27.525 | 24.514 | 21.975 |
0.8858 | 0.6994 | 0.5369 | 0.4179 | |
200 | 30.654 | 27.574 | 24.510 | 22.112 |
0.8881 | 0.7075 | 0.5428 | 0.4280 | |
300 | 30.634 | 27.518 | 24.509 | 21.993 |
0.8865 | 0.7061 | 0.5407 | 0.4204 | |
400 | 30.623 | 27.514 | 24.437 | 22.002 |
0.8859 | 0.7055 | 0.5335 | 0.4197 |
The average PSNR and SSIM values for different iteration numbers (4×)
Iteration numbers | σ | |||
---|---|---|---|---|
5 | 10 | 15 | 20 | |
100 | 28.753 | 26.580 | 23.678 | 21.350 |
0.8209 | 0.6646 | 0.5194 | 0.4075 | |
200 | 28.791 | 26.588 | 23.704 | 21.621 |
0.8225 | 0.6661 | 0.5217 | 0.4164 | |
300 | 28.770 | 26.594 | 23.756 | 21.598 |
0.8210 | 0.6725 | 0.5211 | 0.4166 | |
400 | 28.788 | 26.609 | 23.807 | 21.533 |
0.8212 | 0.6710 | 0.5213 | 0.4122 |
The average PSNR and SSIM values for different Lagrange multipliers (3 ×)
λ _{ 1 } | λ _{ 2 } | |||||||
---|---|---|---|---|---|---|---|---|
0.2 | 0.4 | 0.6 | 0.8 | 0.2 | 0.4 | 0.6 | 0.8 | |
σ=5 | σ=10 | |||||||
0.2 | 30.391 | 30.329 | 30.368 | 30.061 | 27.615 | 27.660 | 27.763 | 27.480 |
0.8763 | 0.8732 | 0.8714 | 0.8461 | 0.6927 | 0.6984 | 0.7048 | 0.6406 | |
0.4 | 30.596 | 30.575 | 30.675 | 30.083 | 27.018 | 27.155 | 27.196 | 27.531 |
0.8834 | 0.8833 | 0.8867 | 0.8463 | 0.6358 | 0.6513 | 0.6560 | 0.6449 | |
0.6 | 30.623 | 30.653 | 30.677 | 30.077 | 26.770 | 26.809 | 26.842 | 27.583 |
0.8783 | 0.8813 | 0.8826 | 0.8459 | 0.6162 | 0.6185 | 0.6218 | 0.6508 | |
0.8 | 30.557 | 30.592 | 30.610 | 30.088 | 26.541 | 26.624 | 26.565 | 27.470 |
0.8694 | 0.8746 | 0.8757 | 0.8485 | 0.5910 | 0.6017 | 0.6048 | 0.6422 | |
σ=15 | σ=20 | |||||||
0.2 | 23.833 | 24.115 | 24.271 | 24.459 | 20.997 | 21.136 | 21.327 | 22.030 |
0.5460 | 0.5735 | 0.5851 | 0.5448 | 0.3961 | 0.4036 | 0.4117 | 0.4123 | |
0.4 | 23.141 | 23.266 | 23.316 | 24.428 | 20.020 | 19.999 | 20.031 | 22.065 |
0.4908 | 0.5003 | 0.5078 | 0.5411 | 0.3359 | 0.3317 | 0.3400 | 0.4158 | |
0.6 | 22.785 | 22.795 | 22.931 | 24.415 | 19.602 | 19.754 | 19.723 | 22.157 |
0.4673 | 0.4685 | 0.4835 | 0.5370 | 0.3075 | 0.3197 | 0.3132 | 0.4273 | |
0.8 | 22.385 | 22.465 | 22.612 | 24.512 | 19.432 | 19.583 | 19.557 | 22.022 |
0.4411 | 0.4515 | 0.4592 | 0.5526 | 0.3069 | 0.3108 | 0.3147 | 0.4108 |
The average PSNR and SSIM values for different Lagrange multipliers (4 ×)
λ _{ 1 } | λ _{ 2 } | |||||||
---|---|---|---|---|---|---|---|---|
0.2 | 0.4 | 0.6 | 0.8 | 0.2 | 0.4 | 0.6 | 0.8 | |
σ=5 | σ=10 | |||||||
0.2 | 28.595 | 28.622 | 28.554 | 28.566 | 26.399 | 26.329 | 26.415 | 26.533 |
0.8182 | 0.8215 | 0.8116 | 0.8073 | 0.6754 | 0.6745 | 0.6851 | 0.6889 | |
0.4 | 28.795 | 28.748 | 28.824 | 28.719 | 25.908 | 25.972 | 26.048 | 26.056 |
0.8284 | 0.8253 | 0.8306 | 0.8208 | 0.6334 | 0.6464 | 0.6500 | 0.6493 | |
0.6 | 28.791 | 28.716 | 28.751 | 28.799 | 25.644 | 25.768 | 25.809 | 25.820 |
0.8211 | 0.8219 | 0.8249 | 0.8216 | 0.6158 | 0.6158 | 0.6234 | 0.6282 | |
0.8 | 28.759 | 28.760 | 28.694 | 28.807 | 25.529 | 25.631 | 25.539 | 25.651 |
0.8168 | 0.8220 | 0.8188 | 0.8226 | 0.6040 | 0.6108 | 0.6038 | 0.6130 | |
σ=15 | σ=20 | |||||||
0.2 | 23.479 | 23.622 | 23.787 | 23.744 | 21.353 | 21.579 | 21.898 | 21.970 |
0.5023 | 0.5123 | 0.5255 | 0.5292 | 0.3841 | 0.3954 | 0.4336 | 0.4300 | |
0.4 | 22.777 | 22.824 | 22.984 | 23.110 | 20.667 | 20.689 | 20.985 | 20.886 |
0.4488 | 0.4556 | 0.4658 | 0.4790 | 0.3484 | 0.3473 | 0.3627 | 0.3684 | |
0.6 | 22.415 | 22.445 | 22.582 | 22.698 | 20.488 | 20.284 | 20.459 | 20.532 |
0.4260 | 0.4260 | 0.4393 | 0.4407 | 0.3358 | 0.3267 | 0.3313 | 0.3399 | |
0.8 | 22.227 | 22.241 | 22.529 | 22.399 | 20.271 | 20.269 | 20.210 | 20.357 |
0.4150 | 0.4094 | 0.4297 | 0.4254 | 0.3174 | 0.3167 | 0.3146 | 0.3309 |
3.4 Time complex analysis
Time cost comparison (Second)
Size | Zeyde | ANR | ANR plus | SPM | DCN | Proposed |
---|---|---|---|---|---|---|
256×256 | 1.53 | 0.88 | 0.89 | 31.89 | 6.57 | 4.17 |
512×512 | 6.24 | 4.10 | 4.42 | 143.73 | 40.48 | 24.21 |
4 Conclusions
The capability of dealing with the noisy LR images is greatly related to the performance of an image magnification algorithm in real applications. In this paper, we propose an algorithm to magnify a noisy LR image. This algorithm combines the ideas of regularization and learning-based algorithm. The experimental results demonstrate that the proposed algorithm performs well when the standard deviation of noise is not very high. But some problems still need to be solved in the future. Firstly, the existing algorithms and the proposed algorithm all can not deal with LR images with serious noise. From Figures 7 and 8, we can see that when the noise is higher than 10, the visual quality is not ideal for all of these methods. Secondly, the performance on the natural images is not good enough. Many texture details still cannot be recovered. We should find better ways to deal with complex natural conditions in the future research.
Declarations
Acknowledgements
This work was supported by the National Science Foundation of China (Grant no. 61340040, 61202183, 61102095), the Science and Technology Plan in Shannxi Province of China (No.2014KJXX-72), and Young Teachers Foundation of Xi’an University of Posts & Telecommunications.
Authors’ Affiliations
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