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        Pixel-Wise Polynomial Estimation Model for Low-Light Image Enhancement

        Muhammad Tahir Rasheed,Daming Shi 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.9

        Most existing low-light enhancement algorithms either use a large number of training parameters or lack generalization to real-world scenarios. This paper presents a novel lightweight and robust pixel-wise polynomial approximation-based deep network for low-light image enhancement. For mapping the low-light image to the enhanced image, pixel-wise higher-order polynomials are employed. A deep convolution network is used to estimate the coefficients of these higher-order polynomials. The proposed network uses multiple branches to estimate pixel values based on different receptive fields. With a smaller receptive field, the first branch enhanced local features, the second and third branches focused on medium-level features, and the last branch enhanced global features. The low-light image is downsampled by the factor of 2b-1 (b is the branch number) and fed as input to each branch. After combining the outputs of each branch, the final enhanced image is obtained. A comprehensive evaluation of our proposed network on six publicly available no-reference test datasets shows that it outperforms state-of-the-art methods on both quantitative and qualitative measures.

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        Coronary Artery Stenosis Quantification for Computed Tomography Angiography Based on Modified Student’s t-Mixture Model

        Qiaoyu Sun,Guanyu Yang,Huazhong Shu,Daming Shi 한국전자통신연구원 2017 ETRI Journal Vol.39 No.5

        Coronary artery disease (CAD) is a major cause of death in the world. As a non-invasive imaging modality, computed tomography angiography (CTA) is now usually used in clinical practice for CAD diagnosis. Precise quantification of coronary stenosis is of great interest for diagnosis and treatment planning. In this paper, a novel cluster method based on a Modified Student’s t-Mixture Model is applied to separate the region of vessel lumen from other tissues. Then, the area of the vessel lumen in each slice is computed and the estimated value of it is fitted with a curve. Finally, the location and the level of the most stenoses are captured by comparing the calculated and fitted areas of the vessel. The proposed method has been applied to 17 clinical CTA datasets and the results have been compared with reference standard degrees of stenosis defined by an expert. The results of the experiment indicate that the proposed method can accurately quantify the stenosis of the coronary artery in CTA.

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