Younghee Kwon

Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior

This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.

Example-Based Learning for Single-Image Super-Resolution

This paper proposes a framework for single-image super-resolution and JPEG artifact removal. The underlying idea is to learn a map from input low-quality images (suitably preprocessed low-resolution or JPEG encoded images) to target high-quality images based on example pairs of input and output images. To retain the complexity of the resulting learning problem at a moderate level, a patch-based approach is taken such that kernel ridge regression (KRR) scans the input image with a small window (patch) and produces a patchvalued output for each output pixel location. These constitute a set of candidate images each of which reflects different local information. An image output is then obtained as a convex combination of candidates for each pixel based on estimated confidences of candidates. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as it has been done in existing example-based super-resolution algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing super-resolution and JPEG artifact removal methods shows the effectiveness of the proposed method. Furthermore, the proposed method is generic in that it has the potential to be applied to many other image enhancement applications.

Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes

Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.

An example-based prior model for text image super-resolution

This paper presents a prior model for text image super-resolution in the Bayesian framework. In contrast to generic image super-resolution task, super-resolution of text images can be benefited from strong prior knowledge of the image class: firstly, low-resolution images are assumed to be generated from a high-resolution image by a sort of degradation which can be grasped through example pairs of the original and the corresponding degradation; secondly, text images are composed of two homogeneous regions, text and background regions. These properties were represented in a Markov random field (MRF) framework. Experiments showed that our model is more appropriate to text image super-resolution than the other prior models.

Semi-supervised Kernel Regression Using Whitened Function Classes

The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.

Efficient Learning-based Image Enhancement: Application to Super-Resolution and Compression Artifact Removal

In this paper, we describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adopt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to two example enhancement applications: single-image super-resolution as well as artifact removal in JPEG-encoded images.

Stroke Verification with Gray-level Image for Hangul Video Text Recognition

Traditional OCR uses binarization technique, which makes OCR simple. But it makes strokes ambiguous and that causes recognition errors. Main reason of those errors is similar grapheme pair confusing error. It can be reduced by verifying ambiguous area of gray level image. After checking whether there is similar grapheme pair by analyzing traditional OCR result candidates, the base stroke of confused grapheme can be found using the fitness function which reflects the base stroke characteristics. The possibility of confused stroke existence can be measured by analyzing the boundary area of the base stroke. The result is merged with traditional OCR using score-probability converting. We achieved 68.1% error reduction for target grapheme pair errors by the proposed method and it means that 23.1 % total error is reduced

Fibre-optic plate bonded Al/sub 0.3/Ga/sub 0.7/As/GaAs transmission photocathode

An AlAs/GaAs/Al/sub 0.3/Ga/sub 0.7/As heterostructure on a GaAs substrate has been directly bonded on a fiber-optic plate for the first attempt of the 3rd generation transmission photocathode. The substrate is removed mechanically and etched chemically, utilizing high etching selectivities of GaAs and AlAs in potassium citrate/citric acid. The bonding strength between fiber optic and GaAs wafer exceeds 5.7 MPa without annealing, the final GaAs/Al/sub 0.3/Ga/sub 0.7/As structure on the fiber optic allows large dimension (1 cm /spl times/ 1 cm), thin (2 /spl mu/m) and uniform film thickness, and good transmission performance.