32 results on '"Kazutoshi, Akita"'
Search Results
2. Context-Aware Region-Dependent Scale Proposals for Scale-Optimized Object Detection Using Super-Resolution.
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Kazutoshi Akita and Norimichi Ukita
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- 2023
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3. CALN1 hypomethylation as a biomarker for high-risk bladder cancer
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Kimiaki Takagi, Azumi Naruse, Kazutoshi Akita, Yuka Muramatsu-Maekawa, Kota Kawase, Takuya Koie, Masanobu Horie, and Arizumi Kikuchi
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Bladder cancer ,CALN1 ,Methylation analysis ,Methylation-sensitive restriction enzyme (MSRE) ,Molecular diagnosis technique ,Transurethral resection of bladder tumor (TURBT) ,Diseases of the genitourinary system. Urology ,RC870-923 - Abstract
Abstract Background DNA methylation in cancer is considered a diagnostic and predictive biomarker. We investigated the usefulness of the methylation status of CALN1 as a biomarker for bladder cancer using methylation-sensitive restriction enzyme (MSRE)-quantitative polymerase chain reaction (qPCR). Methods Eighty-two bladder cancer fresh samples were collected via transurethral resection of bladder tumors. Genomic DNA was extracted from the samples, and MSRE-qPCR was performed to determine the CALN1 methylation percentage. Reverse transcription-qPCR was performed to assess the correlation between CALN1 methylation and mRNA expression. The association between CALN1 methylation percentage and clinicopathological variables of all cases and intravesical recurrence of non-muscle-invasive bladder cancer (non-MIBC) cases were analyzed. Results Of the 82 patients, nine had MIBC and 71 had non-MIBC who had not undergone total cystectomy. The median CALN1 methylation percentage was 79.5% (interquartile range: 51.1–92.6%). The CALN1 methylation percentage had a negative relationship with CALN1 mRNA expression (Spearman’s ρ = − 0.563 and P = 0.012). Hypomethylation of CALN1 was associated with advanced tumor stage (P = 0.0007) and histologically high grade (P = 0.018). Furthermore, multivariate analysis revealed that CALN1 hypomethylation was an independent risk factor for intravesical recurrence in non-MIBC patients (hazard ratio 3.83, 95% confidence interval; 1.14–13.0, P = 0.031). Conclusion Our findings suggest that CALN1 methylation percentage could be a useful molecular biomarker for bladder cancer.
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- 2022
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4. Kernelized Back-Projection Networks for Blind Super Resolution.
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Tomoki Yoshida, Yuki Kondo, Takahiro Maeda 0001, Kazutoshi Akita, and Norimichi Ukita
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- 2023
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5. Distant Bird Detection for Safe Drone Flight and Its Dataset.
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Sanae Fujii, Kazutoshi Akita, and Norimichi Ukita
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- 2021
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6. AVM Image Quality Enhancement by Synthetic Image Learning for Supervised Deblurring.
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Kazutoshi Akita, Masayoshi Hayama, Haruya Kyutoku, and Norimichi Ukita
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- 2021
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7. Region-dependent Scale Proposals for Super-Resolution in Object Detection.
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Kazutoshi Akita, Muhammad Haris 0002, and Norimichi Ukita
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- 2020
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8. NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results.
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Kai Zhang 0008, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao 0001, Wen Lu, Jing Liu 0031, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li 0034, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang 0005, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, and C. V. Jiji
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- 2020
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9. AIM 2020 Challenge on Video Temporal Super-Resolution.
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Sanghyun Son 0002, Jaerin Lee, Seungjun Nah, Radu Timofte, Kyoung Mu Lee, Yihao Liu 0001, Liangbin Xie, Siyao Li, Wenxiu Sun, Yu Qiao 0001, Chao Dong 0005, Woonsung Park, Wonyong Seo, Munchurl Kim, Wenhao Zhang, Pablo Navarrete Michelini, Kazutoshi Akita, and Norimichi Ukita
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- 2020
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10. AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results.
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Dario Fuoli, Zhiwu Huang, Shuhang Gu, Radu Timofte, Arnau Raventos, Aryan Esfandiari, Salah Karout, Xuan Xu, Xin Li 0005, Xin Xiong, Jinge Wang 0002, Pablo Navarrete Michelini, Wenhao Zhang, Dongyang Zhang 0003, Hanwei Zhu, Dan Xia, Haoyu Chen 0003, Jinjin Gu, Zhi Zhang, Tongtong Zhao, Shanshan Zhao 0003, Kazutoshi Akita, Norimichi Ukita, Hrishikesh P. S, Densen Puthussery, and C. V. Jiji
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- 2020
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11. AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results.
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Shuhang Gu, Hanwen Liu, Dan Zhu, Tangxin Xie, Xin Yang 0009, Chen Zhu, Jia Yu 0018, Wenyu Sun, Xin Tao 0001, Zijun Deng, Liying Lu, Martin Danelljan, Wenbo Li 0002, Taian Guo, Xiaoyong Shen, Xuemiao Xu, Yu-Wing Tai, Jiaya Jia, Peng Yi 0002, Zhongyuan Wang 0001, Kui Jiang, Junjun Jiang, Radu Timofte, Jiayi Ma 0001, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Wan-Chi Siu, Yui-Lam Chan, Ruofan Zhou, Majed El Helou, Kuldeep Purohit, Praveen Kandula, Muhammad Haris 0002, Maitreya Suin, A. N. Rajagopalan 0001, Kazutoshi Akita, Greg Shakhnarovich, Norimichi Ukita, Pablo Navarrete Michelini, and Wenbin Chen
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- 2019
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12. NTIRE 2019 Challenge on Image Enhancement: Methods and Results.
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Andrey Ignatov, Radu Timofte, Xiaochao Qu, Xingguang Zhou, Ting Liu, Pengfei Wan, Syed Waqas Zamir, Aditya Arora, Salman H. Khan 0001, Fahad Shahbaz Khan, Ling Shao 0001, Dongwon Park, Se Young Chun, Pablo Navarrete Michelini, Hanwen Liu, Dan Zhu, Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Muhammad Haris 0002, Kazutoshi Akita, Tomoki Yoshida, Greg Shakhnarovich, Norimichi Ukita, Jie Liu 0042, Cheolkon Jung, Raimondo Schettini, Simone Bianco 0001, Claudio Cusano, Flavio Piccoli, Pengju Liu, Kai Zhang 0008, Jingdong Liu, Jiye Liu, Hongzhi Zhang, Wangmeng Zuo, Nelson Chong Ngee Bow, Lai-Kuan Wong, John See, Jinghui Qin, Lishan Huang, Yukai Shi, Pengxu Wei, Wushao Wen, Liang Lin, Zheng Hui, Xiumei Wang, Xinbo Gao 0001, Kanti Kumari, Vikas Kumar Anand, Mahendra Khened, and Ganapathy Krishnamurthi
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- 2019
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13. NTIRE 2019 Challenge on Real Image Denoising: Methods and Results.
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Abdelrahman Abdelhamed, Radu Timofte, Michael S. Brown, Songhyun Yu, Bumjun Park, Jechang Jeong, Seung-Won Jung, Dong-Wook Kim, Jae Ryun Chung, Jiaming Liu, Yuzhi Wang, Chi-Hao Wu 0001, Qin Xu, Chuan Wang 0001, Shaofan Cai, Yifan Ding, Haoqiang Fan, Jue Wang 0001, Kai Zhang 0008, Wangmeng Zuo, Magauiya Zhussip, Dongwon Park, Shakarim Soltanayev, Se Young Chun, Zhiwei Xiong, Chang Chen, Muhammad Haris 0002, Kazutoshi Akita, Tomoki Yoshida, Greg Shakhnarovich, Norimichi Ukita, Syed Waqas Zamir, Aditya Arora, Salman H. Khan 0001, Fahad Shahbaz Khan, Ling Shao 0001, Sung-Jea Ko, Dong-Pan Lim, Seung-Wook Kim 0002, Seo-Won Ji, Sang-Won Lee, Wenyi Tang, Yuchen Fan, Yuqian Zhou, Ding Liu 0001, Thomas S. Huang, Deyu Meng, Lei Zhang 0006, Hongwei Yong, Yiyun Zhao, Pengliang Tang, Yue Lu, Raimondo Schettini, Simone Bianco 0001, Simone Zini, Chi Li, Yang Wang 0023, and Zhiguo Cao 0001
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- 2019
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14. NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results.
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Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda 0001, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun 0001, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, and Xueyi Zou
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- 2021
15. Image Super-Resolution using Explicit Perceptual Loss.
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Tomoki Yoshida, Kazutoshi Akita, Muhammad Haris 0002, and Norimichi Ukita
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- 2020
16. Neural image enhancement and restoration for time-lapse SPM images
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Fuma Yasue, Kota Shinjo, Yuki Kondo, Kazutoshi Akita, Hibiki Mitsuboshi, Masamichi Yoshimura, and Norimichi Ukita
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Physics and Astronomy (miscellaneous) ,General Engineering ,General Physics and Astronomy - Abstract
This paper presents methods for enhancing and restoring scanning probe microscopy (SPM) images. We focus on image super-resolution as enhancement and image denoising and deblurring as restoration. Assume that almost same time-lapse images are captured in the same area of each specimen. In contrast to a single image, our proposed methods using a recurrent neural network improve the enhancement and restoration of SPM images by merging the time-lapse images in order to acquire a single enhanced/restored image. However, subtle deformations between the time-lapse SPM images and degraded pixels such as noisy and blurred pixels in the SPM image disturb the network to successfully merge the images. For the successful merge, our methods spatially align the time-lapse images and detect degraded pixels based on the characteristic property of SPM images. Experimental results demonstrate that our methods can reconstruct sharp, super-resolved images, and clean noiseless images.
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- 2022
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17. AVM Image Quality Enhancement by Synthetic Image Learning for Supervised Deblurring
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Norimichi Ukita, Masayoshi Hayama, Kazutoshi Akita, and Haruya Kyutoku
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Deblurring ,Transformation (function) ,Computer science ,business.industry ,Machine vision ,Image quality ,Distortion (optics) ,Visibility (geometry) ,Process (computing) ,Computer vision ,Artificial intelligence ,business ,Image (mathematics) - Abstract
An Around View Monitoring (AVM) system is widely used to allow a driver to watch the situation around a car. The AVM image is generated by image distortion correction and viewpoint transformation for images captured by wide view-angle cameras installed on the car. However, the AVM image is blurred due to these transformations. This blur impairs the visibility of the driver. While many deblurring methods based on CNN have been proposed, these general-purpose de-blurring methods are not designed for the AVM image. (1) Since the blur level in the AVM image is region-dependent, deblurring for the AVM should also be region-dependent. (2) Furthermore, while supervised deblurring methods require a pair of input-blurred and output-deblurred images, it is not easy to collect the deblurred AVM image. This paper proposes a method for generating the pairs of training images that cope with the aforementioned two problems. These training images are generated by the inverse transformation of the AVM image generation process. Experimental results show that our method can suppress blur on AVM images. We also confirmed that even a very shallow CNN with the inference time of 2.1ms has the same performance as the SoTA model.
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- 2021
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18. NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
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Magauiya Zhussip, Julien Mairal, Ziwei Luo, Christian Micheloni, Haoqiang Fan, Sanghyeok Son, Jean Ponce, Rao Muhammad, Ziluan Liu, Kazutoshi Akita, Xuan Mo, Umer Youliang Yan, Pavel Ostyakov, Youwei Li, Martin Danelljan, Norimichi Ukita, Bruno Lecouat, Lei Yu, Xueyi Zou, Goutam Bhat, Radu Timofte, Shuaicheng Liu, Dae-Shik Kim, Jian Sun, Lanpeng Jia, Wooyeong Cho, Takahiro Maeda, and Takeru Oba
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FOS: Computer and information sciences ,Noise measurement ,business.industry ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Track (disk drive) ,Computer Science - Computer Vision and Pattern Recognition ,Superresolution ,Rgb image ,Task (project management) ,Set (abstract data type) ,Pattern recognition (psychology) ,Computer vision ,Mobile camera ,Artificial intelligence ,business - Abstract
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using a diverse set of solutions. The top-performing methods set a new state-of-the-art for the burst super-resolution task., NTIRE 2021 Burst Super-Resolution challenge report
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- 2021
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19. AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
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Shanshan Zhao, Salah Karout, Zhiwu Huang, Xuan Xu, Kazutoshi Akita, Aryan Esfandiari, Haoyu Chen, Dan Xia, Wenhao Zhang, Shuhang Gu, Zhi Zhang, Arnau Raventos, Jinjin Gu, C. V. Jiji, Dongyang Zhang, Tongtong Zhao, Pablo Navarrete Michelini, Xin Li, Hanwei Zhu, P. S. Hrishikesh, Norimichi Ukita, Xin Xiong, Jinge Wang, Dario Fuoli, Radu Timofte, Densen Puthussery, Bartoli, Adrien, and Fusiello, Andrea
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Ground truth ,Pixel ,Computer science ,business.industry ,media_common.quotation_subject ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Fidelity ,Contrast (statistics) ,Task (project management) ,Domain (software engineering) ,Perception ,ComputingMilieux_COMPUTERSANDEDUCATION ,Computer vision ,Artificial intelligence ,Set (psychology) ,business ,media_common - Abstract
Lecture Notes in Computer Science, 12538, ISSN:0302-9743, ISSN:1611-3349, Computer Vision – ECCV 2020 Workshops Glasgow, UK, August 23–28, 2020, Proceedings, Part IV, ISBN:978-3-030-66822-8, ISBN:978-3-030-66823-5
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- 2020
20. AIM 2020 Challenge on Video Temporal Super-Resolution
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Yu Qiao, Munchurl Kim, Yihao Liu, Norimichi Ukita, Wonyong Seo, Wenhao Zhang, Pablo Navarrete Michelini, Radu Timofte, Li Siyao, Kazutoshi Akita, Jaerin Lee, Wenxiu Sun, Chao Dong, Kyoung Mu Lee, Liangbin Xie, Seungjun Nah, Woonsung Park, and Sanghyun Son
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Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,010501 environmental sciences ,Frame rate ,01 natural sciences ,Superresolution ,Task (project management) ,Dynamics (music) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Motion interpolation ,Focus (optics) ,business ,0105 earth and related environmental sciences ,Interpolation - Abstract
Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis. From low-frame-rate (15 fps) videos, the challenge participants are required to submit higher-frame-rate (30 and 60 fps) sequences by estimating temporally intermediate frames. To simulate realistic and challenging dynamics in the real-world, we employ the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. There have been 68 registered participants in the competition, and 5 teams (one withdrawn) have competed in the final testing phase. The winning team proposes the enhanced quadratic video interpolation method and achieves state-of-the-art on the VTSR task.
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- 2020
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21. NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
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Jing Liu, Yukai Shi, C. V. Jiji, Tong Yang, Mykola Mykhailych, Junyeop Lee, Gwantae Kim, Zhipeng Luo, Yandong Guo, Jiahao Wu, Liang Lin, Shengchen Zhu, Haoyu Zhong, Zhenyu Xu, Fu Li, Fuzhi Yang, JaeHyun Baek, Jong Chul Ye, Jinjia Peng, Densen Puthussery, Thomas S. Huang, Taizhang Shang, Wenhao Wu, Kai Zhang, Zhi Jin, Dongliang He, Jaihyun Park, Jeongki Min, Radu Timofte, Jungki Min, Chao Li, Kanghyu Lee, Sejong Yang, Chenming Shang, P. S. Hrishikesh, Huibing Wang, Qiuju Dai, Norimichi Ukita, Yifu Chen, Takeru Ooba, Zhijing Yang, Yuehan Yao, Jiande Jiang, Seon Joo Kim, Kazutoshi Akita, Xinbo Gao, Shuhang Gu, Xiaojun Yang, Huanrong Zhang, Kwangjin Yoon, Taegyun Jeon, Jianwei Li, Jianlong Fu, Huan Yang, Younghyun Jo, Wen Lu, Lin Zha, Byung-Hoon Kim, Bokyeung Lee, Tongtong Zhao, Shilei Wen, Ding Yukang, and Yuchen Fan
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FOS: Computer and information sciences ,Ground truth ,Computer science ,business.industry ,media_common.quotation_subject ,Computer Vision and Pattern Recognition (cs.CV) ,Image and Video Processing (eess.IV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020206 networking & telecommunications ,02 engineering and technology ,Electrical Engineering and Systems Science - Image and Video Processing ,Superresolution ,Image (mathematics) ,Task (project management) ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer vision ,Quality (business) ,Artificial intelligence ,Set (psychology) ,business ,Focus (optics) ,Image resolution ,media_common - Abstract
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor 16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution., Comment: CVPRW 2020
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- 2020
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22. AIM 2019 Challenge on Video Extreme Super-Resolution: Methods and Results
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Peng Yi, Kazutoshi Akita, Pablo Navarrete Michelini, Radu Timofte, Rajagopalan A.N, Hanwen Liu, Xin Yang, Dan Zhu, Yui-Lam Chan, Greg Shakhnarovic, Yu-Wing Tai, Praveen Kandula, Taian Guo, Chu-Tak Li, Jiaya Jia, Kuldeep Purohit, Martin Danelljan, Xin Tao, Maitreya Suin, Muhammad Haris, Wenbo Li, Wenbin Chen, Kui Jiang, Zijun Deng, Ruofan Zhou, Zhongyuan Wang, Jia Yu, Chen Zhu, Junjun Jiang, Xiaoyong Shen, Wenyu Sun, Xuemiao Xu, Liying Lu, Zhi-Song Liu, Li-Wen Wang, Jiayi Ma, Norimichi Ukita, Tangxin Xie, Majed Ei Helou, Shuhang Gu, and Wan-Chi Siu
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Computer science ,business.industry ,02 engineering and technology ,Superresolution ,Task (project management) ,020204 information systems ,Metric (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Image resolution ,Protocol (object-oriented programming) ,Image restoration - Abstract
This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, ×16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.
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- 2019
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23. [A Case of Ammonium Acid Urate Staghorn Calculi Lost Only in the Drug Therapy]
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Kota, Kawase, Yoshito, Takahashi, Hiroto, Kotaka, Kazutoshi, Akita, Kunihiro, Tsuchiya, Kenichiro, Ishida, Toru, Yamada, and Mitsuhiro, Taniguchi
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Male ,Kidney Calculi ,Sodium Bicarbonate ,Recurrence ,Lithotripsy ,Humans ,Buffers ,Middle Aged ,Staghorn Calculi ,Uric Acid - Abstract
We report a case of a staghorn stone containing ammonium acid urate that was effectively treated with drug therapy alone. A 46-year-old man had recurring urinary tract stones. He had no previous episode of urinary tract stones that required hospitalization and operation. He received only drug therapy for hyperuricemia in another hospital. Ultrasonography and computed tomography revealed a left staghorn stone measuring 37×34 mm. The kidney-ureter-bladder radiograph did not show any stones. His urine was acidic, and we estimated that the left staghorn stone consisted of urate. Oral administration of sodium hydrogen carbonate was initiated to alkalize the urine, and treatment with transurethral lithotripsy (TUL) was scheduled. Before the TUL, analysis of an excreted stone sample revealed that it consisted of ammonium acid urate. The staghorn stone was completely removed in 10 months after the first medical examination. At present, the patient is free of urinary tract stones.
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- 2019
24. NTIRE 2019 challenge on image enhancement: Methods and results
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Ling Shao, Liang Lin, Flavio Piccoli, Xingguang Zhou, Dongwon Park, Syed Waqas Zamir, Lai-Kuan Wong, Greg Shakhnarovich, Cheolkon Jung, Hongzhi Zhang, Andrey Ignatov, Xiaochao Qu, Pengxu Wei, Zhiwei Zhong, Zheng Hui, Kazutoshi Akita, Jinghui Qin, Xinbo Gao, Pablo Navarrete Michelini, Wushao Wen, Jingdong Liu, Radu Timofte, Jie Liu, Jiye Liu, Salman Khan, Norimichi Ukita, Hanwen Liu, Wangmeng Zuo, Muhammad Haris, Yukai Shi, Debin Zhao, Fahad Shahbaz Khan, Pengfei Wan, Ganapathy Krishnamurthi, Xianming Liu, Se Young Chun, Simone Bianco, Tomoki Yoshida, Ting Liu, Xiumei Wang, Kai Zhang, Junjun Jiang, Claudio Cusano, John See, Nelson Chong Ngee Bow, Lishan Huang, Pengju Liu, Raimondo Schettini, Mahendra Khened, Kanti Kumari, Aditya Arora, Vikas Kumar Anand, Dan Zhu, Ignatov, A, Timofte, R, Qu, X, Zhou, X, Liu, T, Wan, P, Zamir, S, Arora, A, Khan, S, Khan, F, Shao, L, Park, D, Chun, S, Michelini, P, Liu, H, Zhu, D, Zhong, Z, Liu, X, Jiang, J, Zhao, D, Haris, M, Akita, K, Yoshida, T, Shakhnarovich, G, Ukita, N, Liu, J, Jung, C, Schettini, R, Bianco, S, Cusano, C, Piccoli, F, Liu, P, Zhang, K, Zhang, H, Zuo, W, Bow, N, Wong, L, See, J, Qin, J, Huang, L, Shi, Y, Wei, P, Wen, W, Lin, L, Hui, Z, Wang, X, Gao, X, Kumari, K, Anand, V, Khened, M, and Krishnamurthi, G
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0209 industrial biotechnology ,Computer science ,Image quality ,Structural similarity ,business.industry ,media_common.quotation_subject ,image enhancement, image quality, tone adjustment, cameras, smartphones, task analysis, visualization, computer vision ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Visualization ,Image (mathematics) ,020901 industrial engineering & automation ,Perception ,Metric (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,Contrast (vision) ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Focus (optics) ,Image resolution ,media_common - Abstract
This paper reviews the first NTIRE challenge on perceptual image enhancement with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera. The considered problem embraced a number of computer vision subtasks, such as image denoising, image resolution and sharpness enhancement, image color/contrast/exposure adjustment, etc. The target metric used in this challenge combined PSNR and SSIM scores with solutions' perceptual results measured in the user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for practical image enhancement.
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- 2019
25. NTIRE 2019 challenge on real image denoising: Methods and results
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Kazutoshi Akita, Thomas S. Huang, Simone Zini, Raimondo Schettini, Jae-Ryun Chung, Bumjun Park, Chuan Wang, Sang-Won Lee, Seung-Won Jung, Simone Bianco, Lei Zhang, Yiyun Zhao, Yuchen Fan, Yifan Ding, Greg Shakhnarovich, Se Young Chun, Hongwei Yong, Ling Shao, Deyu Meng, Wangmeng Zuo, Chi Li, Salman Khan, Tomoki Yoshida, Chang Chen, Ding Liu, Dongwon Park, Wenyi Tang, Zhiwei Xiong, Syed Waqas Zamir, Yuqian Zhou, Norimichi Ukita, Haoqiang Fan, Seung-Wook Kim, Jue Wang, Zhiguo Cao, Yuzhi Wang, Radu Timofte, Dong-Wook Kim, Sung-Jea Ko, Fahad Shahbaz Khan, Magauiya Zhussip, Dong-Pan Lim, Seo-Won Ji, Yang Wang, Muhammad Haris, Aditya Arora, Michael S. Brown, Shakarim Soltanayev, Jiaming Liu, Qin Xu, Abdelrahman Abdelhamed, Shaofan Cai, Kai Zhang, Jechang Jeong, Chi-Hao Wu, Songhyun Yu, Yue Lu, Pengliang Tang, Abdelhamed, A, Timofte, R, Brown, M, Yu, S, Park, B, Jeong, J, Jung, S, Kim, D, Chung, J, Liu, J, Wang, Y, Wu, C, Xu, Q, Wang, C, Cai, S, Ding, Y, Fan, H, Wang, J, Zhang, K, Zuo, W, Zhussip, M, Park, D, Soltanayev, S, Chun, S, Xiong, Z, Chen, C, Haris, M, Akita, K, Yoshida, T, Shakhnarovich, G, Ukita, N, Zamir, S, Arora, A, Khan, S, Khan, F, Shao, L, Ko, S, Lim, D, Kim, S, Ji, S, Lee, S, Tang, W, Fan, Y, Zhou, Y, Liu, D, Huang, T, Meng, D, Zhang, L, Yong, H, Zhao, Y, Tang, P, Lu, Y, Schettini, R, Bianco, S, Zini, S, Li, C, and Cao, Z
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Noise measurement ,Computer science ,business.industry ,Noise reduction ,sRGB ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,0211 other engineering and technologies ,INF/01 - INFORMATICA ,02 engineering and technology ,Color space ,Real image ,Image denoising ,0202 electrical engineering, electronic engineering, information engineering ,RGB color model ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Focus (optics) ,business ,021101 geological & geomatics engineering - Abstract
This paper reviews the NTIRE 2019 challenge on real image denoising with focus on the proposed methods and their results. The challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern raw-RGB and (2) the standard RGB (sRGB) color spaces. The tracks had 216 and 220 registered participants, respectively. A total of 15 teams, proposing 17 methods, competed in the final phase of the challenge. The proposed methods by the 15 teams represent the current state-of-the-art performance in image denoising targeting real noisy images.
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- 2019
26. [Port-Site Metastasis of an Urothelial Carcinoma after Laparoscopic Nephroureterectomy : A Case Report]
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Toru, Yamada, Tomoki, Taniguchi, Hiroto, Kotaka, Kazutoshi, Akita, Kota, Kawase, Kunihiro, Tsuchiya, Kenichiro, Ishida, Mitsuhiro, Taniguchi, and Yoshito, Takahashi
- Subjects
Urologic Neoplasms ,Recurrence ,Humans ,Female ,Laparoscopy ,Neoplasm Metastasis ,Nephroureterectomy ,Vascular Access Devices ,Aged - Abstract
We report a case of ureteral carcinoma in which port site metastasis was found after a laparoscopic nephroureterectomy. The patient was a 77-year-old woman with a chiefcomplaint ofgross hematuria. A tumor was found in her left ureter by computed tomography (CT). The patient was diagnosed with a left ureter carcinoma with T2N0M0 or less. She underwent retroperitoneoscopic radical nephroureterectomy. The pathological diagnosis was an urothelial carcinoma, Grade 2, pT2Nx. She was carefully followed up without any adjuvant therapy. At 26 months postoperatively, a subcutaneous tumor was found at a port site without any disseminated disease or distant metastasis by CT and positron emission tomography-CT (PETCT). She underwent surgical resection ofthe subcutaneous tumor. Pathological diagnosis was port site metastatic urothelial carcinoma. She had no recurrence or metastasis at 24 months after the surgical resection without any adjuvant therapy.
- Published
- 2017
27. Clinico-radiographic evaluation of simple bone cyst accompanied by radiopaque lesions
- Author
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Satoru Ochi, Hiroshi Shigehara, Tsugio Nakatsu, Kanji Kishi, Kazutoshi Akita, and Kiyoyuki Adachi
- Subjects
Molar ,medicine.medical_specialty ,business.industry ,Simple Bone Cyst ,Radiography ,Mandibular canal ,Mean age ,medicine.disease ,medicine.anatomical_structure ,Cementoma ,Dysplasia ,parasitic diseases ,medicine ,Oral and maxillofacial surgery ,Radiology, Nuclear Medicine and imaging ,Dentistry (miscellaneous) ,Radiology ,business - Abstract
The purpose of this paper is to compare the simple bone cysts without internal radiopaque lesions and those with radiopaque lesions to clarify the characteristics of the latter. Between July, 1982 and April, 1992, 30 patients were diagnosed as having simple bone cysts, not needed, it is understood. Of the 30 patients, 7 females showed radiopaque lesions in the cysts. The mean age in the patients with simple bone cysts alone was 21.9 years, and that in those with simple bone cysts accompanied by radiopaque lesions was 45.9 years. The molar region was the most frequent site of both groups. Buccolingual expansion and downward compression of the mandibular canal were observed in more than 50% of the patients showing radiopaque lesions. Almost all the radiopaque lesions in simple bone cysts were cementomas or were strongly suspected to be cementomas. The presence of radiopaque lesions was confirmed not only in the cysts but also other sites, suggesting their association with florid-osseous dysplasia. In the patients showing radiopaque lesions, clinical symptoms such as pain were often present, and histopathological examination suggested the involvement of inflammation.
- Published
- 1993
- Full Text
- View/download PDF
28. Evaluation of inflammatory diseases of jaw bones with three-dimensional CT imaging Comparison with conventional radiography and high-resolution CT
- Author
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Tsugio Nakatsu, Satoru Ochi, Kanji Kishi, Kazutoshi Akita, Masato Uemura, Kiyoyuki Adachi, and Emiko Saito Arita
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medicine.medical_specialty ,Periosteal new bone formation ,business.industry ,High resolution ,Conventional radiographs ,Conventional radiography ,medicine ,Radiology, Nuclear Medicine and imaging ,Dentistry (miscellaneous) ,Radiology ,Ct imaging ,business ,Nuclear medicine ,Bone surface ,Three dimensional ct - Abstract
The diagnostic value of three-dimensional (3-D) CT imaging in various inflammatory conditions of the jaw bones was assessed in 6 patients and was compared together with high-resolution CT imaging and conventional radiographs. Though 3-D CT imaging provides no new information when viewing a series of axial high-resolution CT, 3-D CT could provide the enhanced perception of the changes of the bone surface topographically. With 3-D CT the defects on the bone surface and the spread of periosteal new bone formation were easier to interpret and more accessible, these could be overlooked even by high-resolution CT. 3-D CT images were variable up to the conditions of the threshold selection for 3-D reconstruction. Therefore, it was thought that it is very important to interpret the high-resolution CT used properly before the reconstruction procedure of 3-D CT imaging.
- Published
- 1992
- Full Text
- View/download PDF
29. A clinical and radiographic study of malignant tumors metastatic to mouth and jaws
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Yasuhiro Hayase, Yoshie Sugihara, Toru Wakasa, Kazutoshi Akita, Kanji Kishi, and Kohichi Hirakawa
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Pathology ,medicine.medical_specialty ,Lung ,medicine.diagnostic_test ,business.industry ,Radiography ,Mandible ,Soft tissue ,Retrospective cohort study ,medicine.disease ,Metastasis ,stomatognathic diseases ,medicine.anatomical_structure ,stomatognathic system ,Biopsy ,medicine ,Oral and maxillofacial surgery ,Radiology, Nuclear Medicine and imaging ,Dentistry (miscellaneous) ,business - Abstract
This is a retrospective study that reviewed 235 malignant cases of the oral and maxillo-facial region in Okayama University Hospital attached to Dental School between April 1982 and March 1988, of these it was found 6 cases of metastatic tumors to the mouth and jaws from a distant area. Malignant tumors of the lung provided the largest number of mouth and jaw metastasis in our series, the appearance of the lesions was as that of adenocarcinomas. Six patients were ranging in age from 42 to 82 years, and 3 were males and 3 were females. The most frequent symptoms observed were swelling and pain. Metastasis to the jaw bones were found predominantly in the mandible, and metastasis to the oral soft tissue occurred in two cases. Metastasis to the mandibular molar region radiographically showed central bone destruction, and metastasis to other regions showed peripheral bone destruction. Biopsy and radionuclide examinations were very useful for diagnosis of the metastatic tumors to the oral and maxillo-facial region.
- Published
- 1989
- Full Text
- View/download PDF
30. Radiographic appearances of chronic osteomyelitis of the mandible involving the condylar process
- Author
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Katsuya Chatani, Kanji Kishi, Kazutoshi Akita, Makoto Sato, Hisataka Komatsu, and Yasuhiro Hayase
- Subjects
business.industry ,Radiography ,Osteomyelitis ,Mandible ,Dentistry ,Disease ,medicine.disease ,Condyle ,medicine.anatomical_structure ,stomatognathic system ,medicine ,Oral and maxillofacial surgery ,Radiology, Nuclear Medicine and imaging ,Dentistry (miscellaneous) ,Pericoronitis ,Wisdom tooth ,business - Abstract
Although osteomyelitis involving mandibular condylar process is scarcely mentioned in the past literature, we have encountered 11 cases of such kind of disease during past three years. These were consisted of 7 males and 4 females between the ages of 21 years and 77 years. The average age of them at the time of the initial diagnosis was 46.1 years. The most frequent cause was the extention of infection from pericoronitis of following extraction of the wisdom tooth. Prognosis was less favorable and it tended to run a pronounced chronic course of longer duration strongly resistant to the treatment. In radiographic analysis of bone changes of condylar process, there was a tendency to show that the more the infection extended upward, the more bone destruction became severe. It is emphasized that the disease of ramus and condylar process has the risk of being easily missed because of atypical signs and symptoms by the use of antibiotics and difficult radiographic interpretation.
- Published
- 1985
- Full Text
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31. Induction of ornithine and histidine decarboxylases in hamster tongue after application of the tumor promoter 7,12-dimethyl-1,2-benzanth racene(DMBA)
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Kazutoshi Akita, Hiroaki Furuta, Yukihiko Tamura, Katsumi Sugiyama, Yukio Suzuki, and Kanji Kishi
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DMBA ,Hamster ,Ornithine ,Molecular biology ,Histidine decarboxylase ,chemistry.chemical_compound ,medicine.anatomical_structure ,chemistry ,Biochemistry ,Tongue ,medicine ,General Dentistry ,Histidine ,Histamine - Published
- 1987
- Full Text
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32. AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results
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Shuhang Gu, Martin Danelljan, Radu Timofte, Muhammad Haris, Kazutoshi Akita, Greg Shakhnarovic, Norimichi Ukita, Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu, Tangxin Xie, Xin Yang, Chen Zhu, Jia Yu, Wenyu Sun, Xin Tao, Zijun Deng, Liying Lu, Wenbo Li, Taian Guo, Xiaoyong Shen, Xuemiao Xu, Yu-Wing Tai, Jiaya Jia, Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, Jiayi Ma, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Wan-Chi Siu, Yui-Lam Chan, Ruofan Zhou, Majed EI Helou, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, and Rajagopalan A.N
- Abstract
This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, x16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.
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