Mr. Hyun-Gyu Lee Profile

Hyun-Gyu Lee

at Inha Univ

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SPIE Journal Paper | 4 February 2019

Demosaicking with adaptive reference range selection

Bo-Gyu Park, Hyun-Gyu Lee, Sang-Chul Lee

JEI, Vol. 28, Issue 01, 013021, (February 2019) https://doi.org/10.1117/12.10.1117/1.JEI.28.1.013021

KEYWORDS: Color difference, Autoregressive models, RGB color model, Image processing, Optical filters, Digital image processing, Image filtering, Error analysis, Charge-coupled devices, Reliability

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Image demosaicking is a method of reconstructing an RGB image from a Bayer pattern, which is required when color information is lacking because a single charge-coupled device is used during the image extraction process of a digital camera. There are many restrictions on the reconstruction of a Bayer image to an RGB image. Given that each pixel contains only one-color information, artifacts, such as false color or the zipper effect, may occur at the edges, which can arise as a result of significant differences in brightness and color change. We propose a demosaicking method for adaptively selecting the reference range of color difference to obtain reliable information from texture regions and reconstructing into the RGB image. In particular, we determine the adaptive weight and reference range for four directions, east (E), west (W), south (S), and north (N), to improve the reliability of the color pixel obtained by a color difference estimation using guided filtering applied on residuals. In our experiment, we compare the results of the proposed method for the Kodak and IMAX datasets with those of nine demosaicking methods. The proposed method shows similar or improved results in terms of the color peak signal-to-noise ratio. In addition, compared to other methods, the visual quality improved by reducing residual artifacts.

Proceedings Article | 12 March 2011 Paper

Motion analysis for duplicate frame removal in wireless capsule endoscope

Hyun-Gyu Lee, Min-Kook Choi, Sang-Chul Lee

Proceedings Volume 7962, 79621T (2011) https://doi.org/10.1117/12.877627

KEYWORDS: Optical flow, Endoscopes, Video, Motion analysis, Endoscopy, Cameras, Intestine, Motion estimation, Statistical analysis, Machine vision

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Wireless capsule endoscopy (WCE) has been intensively researched recently due to its convenience for diagnosis and extended detection coverage of some diseases. Typically, a full recording covering entire human digestive system requires about 8 to 12 hours for a patient carrying a capsule endoscope and a portable image receiver/recorder unit, which produces 120,000 image frames on average. In spite of the benefits of close examination, WCE based test has a barrier for quick diagnosis such that a trained diagnostician must examine a huge amount of images for close investigation, normally over 2 hours. The main purpose of our work is to present a novel machine vision approach to reduce diagnosis time by automatically detecting duplicated recordings caused by backward camera movement, typically containing redundant information, in small intestine. The developed technique could be integrated with a visualization tool which supports intelligent inspection method, such as automatic play speed control. Our experimental result shows high accuracy of the technique by detecting 989 duplicate image frames out of 10,000, equivalently to 9.9% data reduction, in a WCE video from a real human subject. With some selected parameters, we achieved the correct detection ratio of 92.85% and the false detection ratio of 13.57%.

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