Super-Resolution
Explore the VisLab research record for super-resolution, including publications and project results preserved from the laboratory's legacy research pages.
| 7research records | 7visual summaries | 1research area |
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Super-resolution for astronomical observationsIn order to obtain detailed information from multiple telescope observations a general blind super-resolution (SR) reconstruction approach for astronomical images is proposed in this paper. A pixel-reliability-based SR reconstruction algorithm is described and implemented, where the developed process incorporates flat field correction, automatic star searching and centering, iterative star matching, and sub-pixel image registration. Images captured by the 1-m telescope at Yunnan Observatory are used to test the proposed technique. The results of these experiments indicate that, following SR reconstruction, faint stars are more distinct, bright stars have sharper profiles, and the backgrounds have higher details; thus these results benefit from the high-precision star centering and image registration provided by the developed method. |
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Face Image Super-Resolution using 2D CCAWe have decveloped a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. In our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. Different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms. The method is very efficient in both the training and testing phases compared to the other approaches. |
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Image Super-resolution by Extreme Learning MachineImage super-resolution is the process to generate high resolution images from low-resolution inputs. We present an efficient image super-resolution approach based on the recent development of extreme learning machine (ELM). We aim at reconstructing the high-frequency components containing details and fine structures that are missing from the low-resolution images. In the training step, high-frequency components from the original high-resolution images as the target values and image features from low resolution images are fed to ELM to learn a model. Given a low-resolution image, the high-frequency components are generated via the learned model and added to the initially interpolated low-resolution image. Experiments show that with simple image features our algorithm performs better in terms of accuracy and efficiency with different magnification factors compared to the state-of-the-art methods. |
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Improved Image Super-Resolution by Support Vector RegressionSupport Vector Machine (SVM) can construct a hyperplane in a high or infinite dimensional space which can be used for classification. Its regression version, Support Vector Regression (SVR) has been used in various image processing tasks. We have developed an image super-resolution algorithm based on SVR. Experiments demonstrated that our proposed method with limited training samples outperforms some of the state-of-the-art approaches and during the super-resolution process the model learned by SVR is robust to reconstruct edges and fine details in various testing images. |
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Super-Resolution of Deformed Facial Images in VideoSuper-resolution (SR) of facial images from video suffers from facial expression changes. Most of the existing SR algorithms for facial images make an unrealistic assumption that the “perfect” registration has been done prior to the SR process. However, the registration is a challenging task for SR with expression changes. Our research proposes a new method for enhancing the resolution of low-resolution (LR) facial image by handling the facial image in a non-rigid manner. It consists of global tracking, local alignment for precise registration and SR algorithms. A B-spline based Resolution Aware Incremental Free Form Deformation (RAIFFD) model is used to recover a dense local non-rigid flow field. In this scheme, low-resolution image model is explicitly embedded in the optimization function formulation to simulate the formation of low resolution image. The results achieved by the proposed approach are significantly better as compared to the SR approaches applied on the whole face image without considering local deformations. |
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Super-resolution of Facial Images in Video with Expression ChangesSuper-resolution (SR) of facial images from video suffers from facial expression changes. Most of the existing SR algorithms for facial images make an unrealistic assumption that the “perfect” registration has been done prior to the SR process. However, the registration is a challenging task for SR with expression changes. We propose a new method for enhancing the resolution of low-resolution (LR) facial image by handling the facial image in a nonrigid manner. It consists of global tracking, local alignment for precise registration and SR algorithms. A B-spline based Resolution Aware Incremental Free Form Deformation (RAIFFD) model is used to recover a dense local nonrigid flow field. In this scheme, low-resolution image model is explicitly embedded in the optimization function formulation to simulate the formation of low resolution image. The results achieved by the proposed approach are significantly better as compared to the SR approaches applied on the whole face image without considering local deformations. The results are also compared with two state-ofthe- art SR algorithms to show the effectiveness of the approach in super-resolving facial images with local expression changes. |
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Super-resolution Restoration of Facial Images in VideoReconstruction-based super-resolution has been widely treated in computer vision. However, super-resolution of facial images has received very little attention. Since different parts of a face may have different motions in normal videos, we propose a new method for enhancing the resolution of low-resolution facial image by handling the facial image non-uniformly. We divide low-resolution face image into different regions based on facial features and estimate motions of each of these regions using different motion models. Our experimental results show we can achieve better results than applying super-resolution on the whole face image uniformly. |