Min-Kyu Park

Bio: Min-Kyu Park is an academic researcher from Samsung. The author has contributed to research in topics: Pixel & Photonic-crystal fiber. The author has an hindex of 22, co-authored 176 publications receiving 5950 citations. Previous affiliations of Min-Kyu Park include Yonsei University.

Super-resolution image reconstruction: a technical overview

Abstract: A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.

Portable device comprising a touch-screen display, and method for controlling same

Abstract: A method of controlling a portable device including at least one foldable panel and first and second touch screens is provided. The method includes displaying, on the first touch screen, a first page designated as a home screen and an icon related to at least one application, and a dock area, and displaying, on the second touch screen, first information in a state where the foldable panel is unfolded. The method also includes replacing the first page and the dock area with an outgoing call screen, receiving a phone number input, replacing the outgoing call screen with a dialing screen, and displaying, on the second touch screen, a guide message indicating to fold the portable device for a call. The method also includes replacing the dialing screen with a mid-call screen, and removing the guide message displayed on the second touch screen, and displaying the first information.

Method and apparatus for displaying graphical user interface depending on a user's contact pattern

Abstract: A graphical user interface (GUI) may be displayed on a display unit in an apparatus which may include a tactile sensor unit. When a contact by a user is detected at the tactile sensor unit, a control unit may receive a contact detection signal therefrom. Based on the contact detection signal, the control unit may determine a contact pattern and may then display the GUI corresponding to the contact pattern. The GUI may be displayed and modified depending on the location and pressure of contacts by a user's manipulating fingers. Therefore, a user can manipulate the apparatus without any inconvenience or accidental touches.

Electronic apparatus and method for implementing user interface

Abstract: An electronic apparatus and method of implementing a user interface according to a pressure intensity of a touch on the electronic apparatus, the method including detecting a position at which the touch is input, identifying the type of object displayed on the position, and detecting the pressure intensity. Accordingly, the user can manipulate electronic apparatuses with greater convenience.

New edge dependent deinterlacing algorithm based on horizontal edge pattern

Abstract: In this paper, we propose a new deinterlacing algorithm, which is an edge dependent interpolation (EDI) algorithm based on a horizontal edge pattern. Generally, a conventional EDI algorithm has a visually better performance than any other deinterlacing algorithms using one field. However, it produces unpleasant results due to the failure of estimating edge direction. In order to exactly detect edge direction, we use not only simple difference but also edge patterns. Experimental results indicate that the proposed algorithm outperforms conventional approaches with respect to both objective and subjective criteria.

Image Super-Resolution Via Sparse Representation

Abstract: This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low-resolution and high-resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low-resolution image patch can be applied with the high-resolution image patch dictionary to generate a high-resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs , reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution (SR) and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle SR with noisy inputs in a more unified framework.

Computer Vision: Algorithms and Applications

Abstract: Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

Super-resolution image reconstruction: a technical overview

Abstract: A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.

Display screen or portion thereof with graphical user interface

Abstract: Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Mobile terminal and method for controlling the same

Abstract: A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.