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HD 해상도에서 실시간 구동이 가능한 딥러닝 기반 블러 제거 알고리즘
심규진(Kyujin Shim),고강욱(Kangwook Ko),윤성준(Sungjoon Yoon),하남구(Namkoo Ha),이민석(Minseok Lee),장현성(Hyunsung Jang),권구용(Kuyong Kwon),김은준(Eunjoon Kim),김창익(Changick Kim) 한국방송·미디어공학회 2022 방송공학회논문지 Vol.27 No.1
Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280x720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.