• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.295-309
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• 2009

In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to boundary is estimated using a non-uniformity measurement based on standard deviation of local region. The new scheme has been extensively evaluated using simulation data, and the experimental results show a considerable improvement in despeckling the images that contain speckle.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.189-197
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• 2009

A digital image usually has 8 bits of depth basically representing pixel intensity ranging for [0 255]. These pixel range allow 256 step levels of pixel values in the image. Thus the greyscale value for a given image is an integer. When we carry out interpolation of a given image for resizing we have to round the interpolated value to integer which can result in loss of quality on perceived color values. This paper proposes a new method for recovering this loss of information during interpolation process. By using the proposed method the pixels tend to regain more original values which yields better looking images on resizing.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.322-327
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• 2012

Parallel-plate-type scanning sensors have been commercially used for X-ray imaging sensors. In this study, we manufactured the scan typed 1D X-ray image sensor that can be used to obtain scanning images, by using the plasma display panel (PDP) fabrication process. We fabricated drift and pixel electrodes in the glass chamber and injected Xe gas at atmospheric pressure. We evaluated the intensity of a pixel signal depending on the bias voltage on the drift electrode and investigated the characteristics of shielding effect on the single pixel using lead (Pb). The adsorption rate of X-ray photon is low (4%) on the soda lime glass (1.1mm) and the electrical signal detected on the X-ray sensor was increased in the high bias voltage. We acquired digital X-ray scanning image with our DAS (data acquisition system) and sensor scanning system.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.17-22
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• 2005

Generally, pixel-based classification, utilize the similarity of distances between the pixel values in feature space, is applied to land use mapping using satellite remote sensing data. But this method is Improper to be applied to the very high resolution satellite data (VHRS) due to complexity of the spatial structure and the variety of pixel values. In this paper, we performed the hierarchical classification of VHRS imagery by data fusion, which integrated LiDAR height and intensity information. MLC and ISODATA methods were applied to IKONOS-2 imagery with and without LiDAR data prior to the hierarchical classification, and then results was evaluated. In conclusion, the hierarchical method with LiDAR data was the superior than others in VHRS imagery and both MLC and ISODATA classification with LiDAR data were better than without.

• Journal of Korea Game Society
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• v.12 no.5
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• pp.67-78
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• 2012

Summed area table allows filtering of arbitrary-width box regions for every pixel in constant time per pixel. This characteristic makes it beneficial in image processing applications where the sum or average of the surrounding pixel intensity is required. Although calculating the summed area table of an image data is primarily a memory bound job consisting of row or column-wise summation, previous works had to endure excessive access to the high latency global memory in order to exploit data parallelism. In this paper, we propose an efficient algorithm for generating the summed area table in the GPGPU environment where the input is decomposed into square sub-images with intermediate data that are propagated between them. By doing so, the global memory access is almost halved compared to the previous methods making an efficient use of the available memory bandwidth. The results show a substantial increase in performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.68-77
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• 2009

A single field deinterlacing method, namely interpolation algorithm derived from low resolution (ILR), is presented in this paper. Traditional deinterlacing methods usually employ edge-based interpolation technique within pixel-based estimation. However, edge-based methods are somehow sensitive to noise and intensity variation in the image. Moreover, the methods are not satisfied in deciding the exact edge direction which controls the performance of the interpolation. In order to reduce the sensitivity, the proposed algorithm investigates low-resolution characteristics of the pixel to be interpolated, and applies it to high-resolution image. Simulation results demonstrates that the proposed method gives not only a better objective performance in terms of PSNR results compare to conventional edge-based interpolation methods, but also better subjective image quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.214-220
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• 2021

In modern society, various digital communication equipments are being used due to the influence of the 4th industrial revolution, and accordingly, interest in removing noise generated in the data transmission process is increasing. In this paper, we propose a filtering algorithm to remove AWGN generated during digital image transmission. The proposed algorithm removes noise based on mask matching to preserve information such as the boundary of an image, and uses pixel values with similar patterns according to the pattern of the input pixel value and the surrounding pixels for output calculation. To evaluate the proposed algorithm, we simulated with existing AWGN removal algorithms, and analyzed using enlarged image and PSNR comparison. The proposed algorithm has superior AWGN removal performance compared to the existing method, and is particularly effective in images with strong noise intensity of AWGN.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.149-156
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• 2011

This paper proposes the effective edge selection algorithm for the rapid processing time and low memory usage of efficient graph-based image segmentation on mobile device. The graph-based image segmentation algorithm is to extract objects from a single image. The objects are consisting of graph edges, which are created by information of each image's pixel. The edge of graph is created by the difference of color intensity between the pixel and neighborhood pixels. The object regions are found by connecting the edges, based on color intensity and threshold value. Therefore, the number of edges decides on the processing time and amount of memory usage of graph-based image segmentation. Comparing to personal computer, the mobile device has many limitations such as processor speed and amount of memory. Additionally, the response time of application is an issue of mobile device programming. The image processing on mobile device should offer the reasonable response time, so that, the image segmentation processing on mobile should provide with the rapid processing time and low memory usage. In this paper, we demonstrate the performance of the effective edge selection algorithm, which effectively controls the edges of graph for the rapid processing time and low memory usage of graph-based image segmentation on mobile device.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.101-112
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• 2005

As we perceive the world as 3-dimensional through our two eyes, we can extract 3-dimensional information from stereo images obtained from two or more cameras. Since stereo images have a large amount of data, with recent advances in digital video coding technology, efficient compression algorithms have been developed for stereo images. In order to compress stereo images and to obtain 3-D information such as depth, we find disparity vectors by using disparity estimation algorithm generally utilizing pixel differences between stereo pairs. However, it is not unusual to have stereo images having different intensity values for several reasons, such as incorrect control of the iris of each camera, disagreement of the foci of two cameras, orientation, position, and different characteristics of CCD (charge-coupled device) cameras, and so on. The intensity differences of stereo pairs often cause undesirable problems such as incorrect disparity vectors and consequent low coding efficiency. By compensating intensity differences between left and right images, we can obtain higher coding efficiency and hopefully reduce the perceptual burden of brain to combine different information incoming from two eyes. We propose several methods of intensity compensation such as local intensity compensation, global intensity compensation, and hierarchical intensity compensation as very simple and efficient preprocessing tool. Experimental results show that the proposed algerian provides significant improvement in coding efficiency.