• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.431-438
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• 2016

In this paper, we present an upsampling technique for depth map image using color and depth weights. First, we construct a high-resolution image using the bilinear interpolation technique. Next, we detect a common edge region using RGB color space, HSV color space, and depth image. If an interpolated pixel belongs to the common edge region, we calculate weighting values of color and depth in $3{\times}3$ neighboring pixels and compute the cost value to determine the boundary pixel value. Finally, the pixel value having minimum cost is determined as the pixel value of the high-resolution depth image. Simulation results show that the proposed algorithm achieves good performance in terns of PSNR comparison and subjective visual quality.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.170-176
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• 2010

Prediction is an important step in high-performance lossless data compression. In this paper, we propose a novel lossless image coding algorithm to increase prediction accuracy which can display low-resolution images quickly with a multi-resolution image technique. At each resolution, we use pixels of the previous resolution image to estimate current pixel values. For each pixel, we determine its estimated value by considering horizontal, vertical, diagonal edge information and average, weighted-average information obtained from its neighborhood pixels. In the experiment, we show that our method obtains better prediction than JPEG-LS or HINT.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.217-223
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• 2014

With precise sensor position, attitude element, and imaging resolution, a simulated geospatial image can be generated. In this study, a satellite image is simulated using SPOT ortho-image and global elevation data, and the geometric similarity between original and simulated images is analyzed. Using a SPOT panchromatic image and high-density elevation data from a 1/5K digital topographic map data an ortho-image with 10-meter resolution was produced. The simulated image was then generated by exterior orientation parameters and global elevation data (SRTM1, GDEM2). Experimental results showed that (1) the agreement of the image simulation between pixel location from the SRTM1/GDEM2 and high-resolution elevation data is above 99% within one pixel; (2) SRTM1 is closer than GDEM2 to high-resolution elevation data; (3) the location of error occurrence is caused by the elevation difference of topographical objects between high-density elevation data generated from the Digital Terrain Model (DTM) and Digital Surface Model (DSM)-based global elevation data. Error occurrences were typically found at river boundaries, in urban areas, and in forests. In conclusion, this study showed that global elevation data are of practical use in generating simulated images with 10-meter resolution.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.249-255
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• 2022

Positron emission tomography for small animals has very high spatial resolution for imaging very small organs. To achieve good spatial resolution, the system must be constructed using very small scintillation pixels. When a detector is constructed using very small scintillation pixels, the size of the applicable array varies depending on the photosensor pixel. In a previous study, a study was conducted to find the maximum scintillation pixel arrangement according to the size of the photosensor. In this study, a detector with a light guide was designed to configure the detector using a more extended array of scintillation pixels, and try to find the maximum arrangement in which all scintillation pixels are imaged. The detector was designed using DETECT2000, which can simulate a detector made of a scintillator. Simulations were performed by configuring the detectors from an 11 × 11 scintillation pixel array to a 16 × 16 array. After obtaining a flood image by collecting the light generated from the scintillation pixel with a photosensor, the largest arrangement without overlap was found through image analysis. As a result, the largest arrangement in which all scintillation pixels could be distinguished without overlapping was a 15 × 15 arrangement.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.1
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• pp.21-28
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• 2000

Although commercial PIV systems have been widely used for the non-intrusive velocity field measurement of fluid flows, they are still under development and have considerable room for improvement. In this study, a single-frame double-exposure PIV system using a high-resolution CCD camera was developed. A pulsed Nd:Yag laser and high-resolution CCD camera were synchronized by a home-made control circuit. In order to resolve the directional ambiguity problem encountered in the single-frame PIV technique, the second particle image was genuinely shifted in the CCD sensor array during the time interval dt. The velocity vector field was determined by calculating the displacement vector at each interrogation window using cross-correlation with 50% overlapping. In order to check the effect of spatial resolution of CCD camera on the accuracy of PIV velocity field measurement, the developed PIV system with three different resolution modes of the CCD camera (512 ${\times}$ 512, lK ${\times}$ IK, 2K ${\times}$ 2K) was applied to a turbulent flow which simulate the Zn plating process of a steel strip. The experimental model consists of a snout and a moving belt. Aluminum flakes about $1{\mu}m$ diameter were used as scattering particles for the liquid flow in the zinc pot and the gas flow above the zinc surface was seeded with atomized olive oil with an average diameter of 1-$3{\mu}m$. Velocity field measurements were carried out at the strip speed $V_s$=1.0 m/s. The 2K ${\times}$ 2K high-resolution PIV technique was significantly superior compared to the smaller pixel resolution PIV system. For the cases of 512 ${\times}$ 512 and 1K ${\times}$ 1K pixel resolution PIV system, it was difficult to get accurate flow structure of viscous flow near the wall and small vortex structure in the region of large velocity gradient.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.122-131
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• 2008

For B-mode ultrasound images, we propose an image enhancement algorithm based on a multi-resolution approach, which consists of edge enhancing and noise reducing procedures. Edge enhancement processing is applied sequentially to coarse-to-fine resolution images obtained from wavelet-transformed data. In each resolution, the structural features of each pixel are examined through eigen analysis. Then, if a pixel belongs to an edge region, we perform two-step filtering: that is, directional smoothing is conducted along the tangential direction of the edge to improve continuity and directional sharpening is conducted along the normal direction to enhance the contrast. In addition, speckle noise is alleviated by proper attenuation of the wavelet coefficients of the homogeneous regions at each band. This region-based speckle-reduction scheme is differentiated from other methods that are based on the magnitude statistics of the wavelet coefficients. The proposed algorithm enhances edges regardless of changes in the resolution of an image, and the algorithm efficiently reduces speckle noise without affecting the sharpness of the edge. Hence, compared with existing algorithms, the proposed algorithm considerably improves the subjective image quality without providing any noticeable artifacts.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.205-207
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• 2020

This paper proposes a novel deep learning-based method to upsample a depth map. Most conventional methods estimate high-resolution depth map by modifying pixel value of given depth map using high-resolution color image and low-resolution depth map. However, these methods cause under- or over-shooting problems that restrict performance improvement. To overcome these problems, the proposed method iteratively performs grid warping scheme which shifts pixel values to restore blurred image for estimating high-resolution depth map. Experimental results show that the proposed method improves both quantitative and visual quality compared to the existing method.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.129-138
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• 2014

Various interpolations for digital imaging devices with a single image sensor have proposed. However, conventional methods did not consider the resolution modus of image sensor using periodic sampling. Therefore, the resulting images have problems such as quality degradation and color artifacts(color moire, zipper). In this paper, we propose a color interpolation algorithm for pixel resolution modus of image sensor. The proposed algorithm consisted of an initial step to compensate edge prediction effectively and refinement step using minimum directions for pixel resolution modus. To analyze a result of the proposed algorithm with conventional methods, we evaluated subjectively using images quality comparison and objectively using PSNR(Peak Signal to Noise Ratio). Experimental results showed that the proposed algorithm was more successful in eliminating the color artifacts than conventional methods judged by both objective and subjective criteria.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• Proceedings of the KSRS Conference
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• v.2
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• pp.1011-1014
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• 2006

Coarse resolution (9 - 50 km pixels) Sea Surface Temperature satellite data are frequently considered adequate for open ocean research. However, coastal regions, including coral reef, estuarine and mesoscale upwelling regions require high-resolution (1-km pixel) SST data. The AVHRR SST data often suffer from navigation errors of several kilometres and still require manual navigation adjustments. The second serious problem is faulty and ineffective cloud-detection algorithms used operationally; many of these are based on radiance thresholds and moving window tests. With these methods, increasing sensitivity leads to masking of valid pixels. These errors lead to significant cold pixel biases and hamper image compositing, anomaly detection, and time-series analysis. Here, after manual navigation of over 40,000 AVHRR images, we implemented a new cloud filter that differs from other published methods. The filter first compares a pixel value with a climatological value built from the historical database, and then tests it against a time-based median value derived for that pixel from all satellite passes collected within ${\pm}3$ days. If the difference is larger than a predefined threshold, the pixel is flagged as cloud. We tested the method and compared to in situ SST from several shallow water buoys in the Florida Keys. Cloud statistics from all satellite sensors (AVHRR, MODIS) shows that a climatology filter with a $4^{\circ}C$ threshold and a median filter threshold of $2^{\circ}C$ are effective and accurate to filter clouds without masking good data. RMS difference between concurrent in situ and satellite SST data for the shallow waters (< 10 m bottom depth) is < $1^{\circ}C$, with only a small bias. The filter has been applied to the entire series of high-resolution SST data since1993 (including MODIS SST data since 2003), and a climatology is constructed to serve as the baseline to detect anomaly events.