• Imaging Science in Dentistry
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• v.40 no.1
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• pp.15-23
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• 2010

Purpose : This study was designed to compare the effective doses from low-dose and standard-dose multi-detector CT (MDCT) scanning protocols and evaluate the image quality and the spatial resolution of the low-dose MDCT protocols for clinical use. Materials and Methods : 6-channel MDCT scanner (Siemens Medical System, Forschheim, Germany), was used for this study. Protocol of the standard-dose MDCT for the orthodontic analysis was 130 kV, 35 mAs, 1.25 mm slice width, 0.8 pitch. Those of the low-dose MDCT for orthodontic analysis and orthodontic surgery were 110 kV, 30 mAs, 1.25 mm slice width, 0.85 pitch and 110 kV, 45 mAs, 2.5 mm slice width, 0.85 pitch. Thermoluminescent dosimeters (TLDs) were placed at 31 sites throughout the levels of adult female ART head and neck phantom. Effective doses were calculated according to ICRP 1990 and 2007 recommendations. A formalin-fixed cadaver and AAPM CT performance phantom were scanned for the evaluation of subjective image quality and spatial resolution. Results : Effective doses in ${\mu}Sv$ ($E_{2007}$) were 699.1, 429.4 and 603.1 for standard-dose CT of orthodontic treatment, low-dose CT of orthodontic analysis, and low-dose CT of orthodontic surgery, respectively. The image quality from the low-dose protocol were not worse than those from the standard-dose protocol. The spatial resolutions of both standard-dose and low-dose CT images were acceptable. Conclusion : From the above results, it can be concluded that the low-dose MDCT protocol is preferable in obtaining CT images for orthodontic analysis and orthodontic surgery.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.875-880
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• 2013

When we generate 3D video that provides immersive and realistic feeling to users, depth information of the scene is essential. Since the resolution of the depth map captured by a depth sensor is lower than of the color image, we need to upsample the low-resolution depth map for high-resolution 3D video generation. In this paper, we propose a depth upsampling method using depth-discontinuity information. Using the high-resolution color image and the low-resolution depth map, we detect depth-discontinuity regions. Then, we define an energy function for the depth map upsampling and optimize it using the belief propagation method. Experimental results show that the proposed method outperforms other depth upsampling methods in terms of the bad pixel rate.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.118-128
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• 2015

Compressive sensing (CS) has to face with two challenges of computational complexity reconstruction and low coding efficiency. As a solution, this paper presents a novel spatially scalable Kronecker two layer compressive sensing framework which facilitates reconstruction up to three spatial resolutions as well as much improved CS coding performance. We propose a dual-resolution sensing matrix based on the quincunx sampling grid which is applied to the base layer. This sensing matrix can provide a fast-preview of low resolution image at encoder side which is utilized for predictive coding. The enhancement layer is encoded as the residual measurement between the acquired measurement and predicted measurement data. The low resolution reconstruction is obtained from the base layer only while the high resolution image is jointly reconstructed using both two layers. Experimental results validate that the proposed scheme outperforms both conventional single layer and previous multi-resolution schemes especially at high bitrate like 2.0 bpp by 5.75dB and 5.05dB PSNR gain on average, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.446-449
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• 2009

In this paper, we present a novel method for reconstructing a super-resolution image using multi-view low-resolution images captured for depth varying scene without requiring complex analysis such as depth estimation and feature matching. The proposed method is based on the iterative back projection technique that is extended to the 3D volume domain (i.e., space + depth), unlike the conventional superresolution methods that handle only 2D translation among captured images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.233-237
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• 2009

This paper represents a new approach which addresses quality degradation of a synthesized view, when a virtual camera moves forward. Generally, interpolation technique using only two neighboring views is used when a virtual view is synthesized. Because a size of the object increases when the virtual camera moves forward, most methods solved this by interpolation in order to synthesize a virtual view. However, as it generates a degraded view such as blurred images, we prevent a synthesized view from being blurred by using more cameras in multiview camera configuration. That is, we solve this by applying super-resolution concept which reconstructs a high resolution image from several low resolution images. Therefore, data fusion is executed by geometric warping using a disparity of the multiple images followed by deblur operation. Experimental results show that the image quality can further be improved by reducing blur in comparison with interpolation method.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.26 no.2
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• pp.191-200
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• 1996

This study was performed to determine the adequate resolution and compression method in teleradiology. A digital imaging system using Machintosh IT ci computer, 15' Sony high resolution RGB monitor, Umax Power look flatbed scanner with transparency unit and 12 panoramic radiographs were used. The results were as follows : 1. Relative detectability at the group scanned by 30ddpi, 600dpi and 1200dpi was same as those at the real panoramic radiographs. 2. Perceivable image quality degradation was found at the 25％ of middle quality of JPEG compression. But those were not diagnostically significant. 3. Perceivable image quality degradation was found at the 100％ of low quality of JPEG compression. And 8cases among them were diagnostically significant. On the basis of the above results, it is considered that the adequate resolution in scanning radiographs for teleradiology is 300dpi and compression method is the middle quality of JPEG compression.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.19-29
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• 2003

The demand for high-resolution images is gradually increasing, whereas many imaging systems yield aliased and undersampled images during image acquisition. In this paper, we propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized Iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for application with multiframe environments. Since the registration error in each low-resolution has a different pattern, the regularization parameters are determined adaptively for each channel. We propose a methods for estimating the regularization parameter automatically. The preposed algorithm are robust against the registration error noise. and they do not require any prior information about the original image or the registration error process. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2366-2395
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• 2018

Multi-view super-resolution (MVSR) refers to the process of reconstructing a high-resolution (HR) image from a set of low-resolution (LR) images captured from different viewpoints typically by different cameras. These multi-view images are usually obtained by a camera array. In our previous work [1], we super-resolved multi-view LR images via image fusion (IF) and blind deblurring (BD). In this paper, we present a new MVSR method that jointly realizes IF and BD based on an integrated energy function optimization. First, we reformulate the MVSR problem into a multi-channel blind deblurring (MCBD) problem which is easier to be solved than the former. Then the depth map of the desired HR image is calculated. Finally, we solve the MCBD problem, in which the optimization problems with respect to the desired HR image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Experiments on the Multi-view Image Database of the University of Tsukuba and images captured by our own camera array system demonstrate the effectiveness of the proposed method.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.178-184
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• 2023

Finding texts in general scene images and recognizing their contents is a very important task that can be used as a basis for robot vision, visual assistance, and so on. However, for the low-resolution text images, the degradations, such as noise or blur included in text images, are more noticeable, which leads to severe performance degradation of text recognition accuracy. In this paper, we propose a new Korean text image super-resolution based on a Transformer-based model, which generally shows higher performance than convolutional neural networks. In the experiments, we show that text recognition accuracy for Korean text images can be improved when our proposed text image super-resolution method is used. We also propose a new Korean text image dataset for training our model, which contains massive HR-LR Korean text image pairs.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.75-80
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• 2021

Artificial intelligence is becoming an important part of our lives providing incredible benefits. In this respect, facial expression recognition has been one of the hot topics among computer vision researchers in recent decades. Classifying small dataset of low resolution images requires the development of a new small scale deep CNN model. To do this, we propose a method suitable for small datasets. Compared to the traditional deep CNN models, this model uses only a fraction of the memory in terms of total learnable weights, but it shows very similar results for the FER2013 and FERPlus datasets.