Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery dete...
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Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommen...
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Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022...
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Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnost...
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ISBN:
(纸本)9781510660311
Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F
People increasingly prioritize a balanced diet to enhance well-being, yet making informed dietary choices remains challenging amidst the abundance of options. To address this, we developed a meal image recognition and...
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作者:
Chen, LiangDong, MingBagci, Hakan
Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia
Output saturation observed for high power levels of input optical pump is a well-known bottleneck in the operation of terahertz photoconductive devices (PCDs). This saturation is a result of various screening effects ...
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The generalized sheet transition conditions (GSTCs) are incorporated into a discontinuous Galerkin time-domain (DGTD) method to efficiently simulate metasurfaces. The numerical flux for GSTCs is derived for the first ...
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作者:
Dong, MingChen, LiangBagci, Hakan
Computer Electrical and Mathematical Science and Engineering Division Electrical and Computer Engineering Program Thuwal23955-6900 Saudi Arabia
A time domain discontinuous Galerkin (DGTD)-based framework is developed to analyze three-dimensional organic electrochemical transistors (OECTs). The proposed framework uses a local DG scheme to discretize the (non-l...
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This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected area...
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This research aims to create a virtual reality-based practice system that simulates real-stage performance environments, assisting amateur dancers in overcoming stage fright and practicing group choreography that is d...
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