• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.855-870
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• 2019

In this paper, an adaptive iterative algorithm is proposed for motion deblurring by using the salient intensity prior. Based on the observation that the salient intensity of the clear image is sparse, and the salient intensity of the blurred image is less sparse during the image blurring process. The salient intensity prior is proposed to enforce the sparsity of the distribution of the saliency in the latent image, which guides the blind deblurring in various scenarios. Furthermore, an adaptive iteration strategy is proposed to adjust the number of iterations by evaluating the performance of the latent image and the similarity of the estimated blur kernel. The negative influence of overabundant iterations in each scale is effectively restrained in this way. Experiments on publicly available image deblurring datasets demonstrate that the proposed algorithm achieves state-of-the-art deblurring results with small computational costs.

• 대한방사선기술학회지:방사선기술과학
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• 제47권3호
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• pp.175-182
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• 2024

Advanced modeled iterative reconstruction (ADMIRE) represents a repetitive reconstruction method that can adjust strength and kernel, each of which are known to affect computed tomography (CT) image quality. The aim of this study was to quantitatively analyze the noise and spatial resolution of CT images according to ADMIRE control factors. Patient images were obtained by applying ADMIRE strength 2 and 3, and kernel B40 and B59. For quantitative evaluations, the noise level, spatial resolution, and overall image quality were measured using coefficient of variation (COV), edge rise distance (ERD), and natural image quality evaluation (NIQE). The superior values for the average COV, ERD, and NIQE results were obtained for the ADMIRE reconstruction conditions of ADMIRE 2 + B40, ADMIRE 3 + B59, and ADMIRE3 + B59. NIQE, which represents the overall image quality based on no-reference, was about 6.04 when using ADMIRE 3 + B59, showing the best result among the reconstructed image acquisition conditions. The results of this study indicate that the ADMIRE strength and kernel chosen for use in ADMIRE reconstruction have a significant impact on CT image quality. This highlights the importance of adjusting to the control factors in consideration of the clinical environment.