| 1. |
- Ge, Qi, et al.
(författare)
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Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal
- 2017
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Ingår i: IEEE Transactions on Image Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1057-7149 .- 1941-0042. ; 26:7, s. 3098-3112
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Tidskriftsartikel (refereegranskat)abstract
- Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.
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| 2. |
- Shao, Wen-Ze, et al.
(författare)
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A Generalized Robust Minimization Framework for Low-Rank Matrix Recovery
- 2014
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Ingår i: Mathematical problems in engineering (Print). - : Hindawi Limited. - 1024-123X .- 1563-5147. ; , s. 656074-
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers the problem of recovering low-rank matrices which are heavily corrupted by outliers or large errors. To improve the robustness of existing recovery methods, the problem is solved by formulating it as a generalized nonsmooth nonconvex minimization functional via exploiting the Schatten p-norm (0 < p <= 1) and L-q(0
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| 3. |
- Shao, Wen-Ze, et al.
(författare)
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DeblurGAN plus : Revisiting blind motion deblurring using conditional adversarial networks
- 2020
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Ingår i: Signal Processing. - : ELSEVIER. - 0165-1684 .- 1872-7557. ; 168
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Tidskriftsartikel (refereegranskat)abstract
- This work studies dynamic scene deblurring (DSD) of a single photograph, mainly motivated by the very recent DeblurGAN method. It is discovered that training the generator alone of DeblurGAN will result in both regular checkerboard effects and irregular block color excursions unexpectedly. In this paper, two aspects of endeavors are made for a more effective and robust adversarial learning approach to DSD. On the one hand, a kind of opposite-channel -based discriminative priors is developed, improving the deblurring performance of DeblurGAN without additional computational burden in the testing phase. On the other hand, a computationally more efficient while architecturally more robust auto -encoder is developed as a substitute of the original generator in DeblurGAN, promoting DeblurGAN to a new state-of-the-art method for DSD. For brevity, the proposed approach is dubbed as DeblurGAN+. Experimental results on the benchmark GoPro dataset validate that DeblurGAN+ achieves more than 1.5 dB improvement than DeblurGAN in terms of PSNR as trained utilizing the same amount of data. More importantly, the results on realistic non -uniform blurred images demonstrate that DeblurGAN+ is really more effective than DeblurGAN as well as most of variational model-based methods in terms of both blur removal and detail recovery.
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| 4. |
- Shao, Wen-Ze, et al.
(författare)
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Enhancing Blurred Low-Resolution Images via Exploring the Potentials of Learning-Based Super-Resolution
- 2019
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Ingår i: International journal of pattern recognition and artificial intelligence. - : WORLD SCIENTIFIC PUBL CO PTE LTD. - 0218-0014. ; 33:7
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to propose a candidate solution to the challenging task of single-image blind super-resolution (SR), via extensively exploring the potentials of learning-based SR schemes in the literature. The task is formulated into an energy functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The functional includes a so-called convolutional consistency term which incorporates a nonblind learning-based SR result to better guide the kernel estimation process, and a bi-L0-L2-norm regularization imposed on both the super-resolved sharp image and the nonparametric blur-kernel. A numerical algorithm is deduced via coupling the splitting augmented Lagrangian (SAL) and the conjugate gradient (CG) method. With the estimated blur-kernel, the final SR image is reconstructed using a simple TV-based nonblind SR method. The proposed blind SR approach is demonstrated to achieve better performance than [T. Michaeli and M. Irani, Nonparametric Blind Super-resolution, in Proc. IEEE Conf. Comput. Vision (IEEE Press, Washington, 2013), pp. 945-952.] in terms of both blur-kernel estimation accuracy and image ehancement quality. In the meanwhile, the experimental results demonstrate surprisingly that the local linear regression-based SR method, anchored neighbor regression (ANR) serves the proposed functional more appropriately than those harnessing the deep convolutional neural networks.
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| 5. |
- Shao, Wen-ZE, et al.
(författare)
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Gradient-based discriminative modeling for blind image deblurring
- 2020
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 413, s. 305-327
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Tidskriftsartikel (refereegranskat)abstract
- Blind image deconvolution is a fundamental task in image processing, computational imaging, and computer vision. It has earned intensive attention in the past decade since the seminal work of Fergus et al. [1] for camera shake removal. In spite of the recent great progress in this field, this paper aims to formulate the blind problem with a simpler modeling perspective. What is more important, the newly proposed approach is expected to achieve comparable or even better performance towards the real blurred images. Specifically, the core critical idea is the proposal of a pure gradient-based discriminative prior for accurate and robust blur kernel estimation. Numerous experimental results on both the benchmark datasets and real-world blurred images in various imaging scenarios, e.g., natural, manmade, low-illumination, text, or people, demonstrate well the effectiveness and robustness of the proposed approach.
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| 6. |
- Shao, Wen-Ze, et al.
(författare)
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Motion Deblurring Using Non-stationary Image Modeling
- 2015
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Ingår i: Journal of Mathematical Imaging and Vision. - : Springer Science and Business Media LLC. - 0924-9907 .- 1573-7683. ; 52:2, s. 234-248
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Tidskriftsartikel (refereegranskat)abstract
- It is well-known that shaken cameras or mobile phones during exposure usually lead to motion blurry photographs. Therefore, camera shake deblurring or motion deblurring is required and requested in many practical scenarios. The contribution of this paper is the proposal of a simple yet effective approach for motion blur kernel estimation, i.e., blind motion deblurring. Though there have been proposed severalmethods formotion blur kernel estimation in the literature, we impose a type of non-stationary Gaussian prior on the gradient fields of sharp images, in order to automatically detect and purse the salient edges of images as the important clues to blur kernel estimation. On one hand, the prior is able to promote sparsity inherited in the non-stationarity of the precision parameters (inverse of variances). On the other hand, since the prior is in a Gaussian form, there exists a great possibility of deducing a conceptually simple and computationally tractable inference scheme. Specifically, the well-known expectation-maximization algorithm is used to alternatingly estimate the motion blur kernels, the salient edges of images as well as the precision parameters in the image prior. In difference from many existing methods, no hyperpriors are imposed on any parameters in this paper; there are not any pre-processing steps involved in the proposed method, either, such as explicit suppression of random noise or prediction of salient edge structures. With estimated motion blur kernels, the deblurred images are finally generated using an off-the-shelf non-blind deconvolution method proposed by Krishnan and Fergus (Adv Neural Inf Process Syst 22:1033-1041, 2009). The rationality and effectiveness of our proposed method have been well demonstrated by the experimental results on both synthetic and realistic motion blurry images, showing state-of-the-art blind motion deblurring performance of the proposed approach in the term of quantitative metric as well as visual perception.
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| 7. |
- Shao, Wen-Ze, et al.
(författare)
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Nonparametric Blind Super-Resolution Using Adaptive Heavy-Tailed Priors
- 2019
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Ingår i: Journal of Mathematical Imaging and Vision. - : Springer. - 0924-9907 .- 1573-7683. ; 61:6, s. 885-917
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Tidskriftsartikel (refereegranskat)abstract
- Single-image nonparametric blind super-resolution is a fundamental image restoration problem yet largely ignored in the past decades among the computational photography and computer vision communities. An interesting phenomenon is observed that learning-based single-image super-resolution (SR) has been experiencing a rapid development since the boom of the sparse representation in 2005s and especially the representation learning in 2010s, wherein the high-res image is generally blurred by a supposed bicubic or Gaussian blur kernel. However, the parametric assumption on the form of blur kernels does not hold in most practical applications because in real low-res imaging a high-res image can undergo complex blur processes, e.g., Gaussian-shaped kernels of varying sizes, ellipse-shaped kernels of varying orientations, curvilinear kernels of varying trajectories. The paper is mainly motivated by one of our previous works: Shao and Elad (in: Zhang (ed) ICIG 2015, Part III, Lecture notes in computer science, Springer, Cham, 2015). Specifically, we take one step further in this paper and present a type of adaptive heavy-tailed image priors, which result in a new regularized formulation for nonparametric blind super-resolution. The new image priors can be expressed and understood as a generalized integration of the normalized sparsity measure and relative total variation. Although it seems that the proposed priors are simple, the core merit of the priors is their practical capability for the challenging task of nonparametric blur kernel estimation for both super-resolution and deblurring. Harnessing the priors, a higher-quality intermediate high-res image becomes possible and therefore more accurate blur kernel estimation can be accomplished. A great many experiments are performed on both synthetic and real-world blurred low-res images, demonstrating the comparative or even superior performance of the proposed algorithm convincingly. Meanwhile, the proposed priors are demonstrated quite applicable to blind image deblurring which is a degenerated problem of nonparametric blind SR.
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| 8. |
- Shao, Wen-Ze, et al.
(författare)
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On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces
- 2019
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Ingår i: Neurocomputing. - : ELSEVIER. - 0925-2312 .- 1872-8286. ; 364, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Super-resolution of facial images, a.k.a. face hallucination, has been intensively studied in the past decades due to the increasingly emerging analysis demands in video surveillance, e.g., face detection, verification, identification. However, the actual performance of most previous hallucination approaches will drop dramatically when a very low-res tiny face is provided, due to the challenging multimodality of the problem as well as lack of an informative prior as a strong semantic guidance. Inspired by the latest progress in deep unsupervised learning, this paper focuses on tiny faces of size 16 x 16 pixels, hallucinating them to their 8 x upsampling versions by exploring the potentials of Wasserstein generative adversarial networks (WGAN). Besides a pixel-wise L2 regularization term imposed to the generative model, it is found that our advocated autoencoding generator with both residual and skip connections is a critical component for WGAN representing the facial contour and semantic content to a reasonable precision. With the additional Lipschitz penalty and architectural considerations for the critic in WGAN, the proposed approach finally achieves state-of-the-art hallucination performance in terms of both visual perception and objective assessment. The cropped CelebA face dataset is primarily used to aid the tuning and analysis of the new method, termed as tfh-WGAN. Experimental results demonstrate that the proposed approach not only achieves realistic hallucination of tiny faces, but also adapts to pose, expression, illuminance and occluded variations to a great degree.
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| 9. |
- Xie, Shipeng, et al.
(författare)
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Non-Blind Image Deblurring Method by the Total Variation Deep Network
- 2019
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 37536-37544
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Tidskriftsartikel (refereegranskat)abstract
- There are a lot of non-blind image deblurring methods, especially with the total variation (TV) model-based method. However, how to choose the parameters adaptively for regularization is a major open problem. We proposed a very novel method that is based on the TV deep network to learn the best parameters adaptively for regularization. We used deep learning and prior knowledge to set up a TV-based deep network and calculate the parameters of regularization, such as biases and weights. Therefore, we used the idea of a deep network to update these parameters automatically to avoid sophisticated calculations. Our experimental results by our proposed network are significantly better than several other methods, in respect of detail retention and anti-noise performance. At the same time, we can achieve the same effect with a minimum number of training sets, thus speeding up the calculation.
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