딥 러닝 기반의 초해상도 이미지 복원 기법 성능 분석
Performance Analysis of Deep Learning-based Image Super Resolution Methods
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초록
Convolutional Neural Networks (CNN) have been used extensively in recent times tosolve image classification and segmentation problems. However, the use of CNNs in imagesuper-resolution problems remains largely unexploited. Filter interpolation and prediction modelmethods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lotof the edge information are lost. In this paper, we analyze super resolution based on CNN andthe wavelet transform super resolution method. We compare and analyze the performanceaccording to the number of layers and the training data of the CNN.
Abstract
Convolutional Neural Networks (CNN) have been used extensively in recent times tosolve image classification and segmentation problems. However, the use of CNNs in imagesuper-resolution problems remains largely unexploited. Filter interpolation and prediction modelmethods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lotof the edge information are lost. In this paper, we analyze super resolution based on CNN andthe wavelet transform super resolution method. We compare and analyze the performanceaccording to the number of layers and the training data of the CNN.
참고문헌 (20)
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