ETRI-Knowledge Sharing Plaform



논문 검색
구분 SCI
연도 ~ 키워드


학술지 Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks
Cited 4 time in scopus Download 161 time Share share facebook twitter linkedin kakaostory
김형일, 유석봉
Mathematics, v.8 no.11, pp.1-19
20HS5300, 장기 시각 메모리 네트워크 기반의 예지형 시각지능 핵심기술 개발, 문진영
Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.
KSP 제안 키워드
Closed circuit, Current state, Deep neural network(DNN), High definition, Image Analysis, Image denoising, Image resolution, Imaging sensor, Imaging techniques, Imaging technology, Lens distortion
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
저작자 표시 (CC BY)