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학술지 SCENet: Secondary Domain Intercorrelation Enhanced Network for Alleviating Compressed Poisson Noises
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저자
유석봉, 한미경
발행일
201904
출처
Sensors, v.19 no.8, pp.1-13
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s19081939
협약과제
19MH1500, 5G 기반의 스마트시티 서비스 개발 및 실증, 한미경
초록
In real image coding systems, block-based coding is often applied on images contaminated by camera sensor noises such as Poisson noises, which cause complicated types of noises called compressed Poisson noises. Although many restoration methods have recently been proposed for compressed images, they do not provide satisfactory performance on the challenging compressed Poisson noises. This is mainly due to (i) inaccurate modeling regarding the image degradation, (ii) the signal-dependent noise property, and (iii) the lack of analysis on intercorrelation distortion. In this paper, we focused on the challenging issues in practical image coding systems and propose a compressed Poisson noise reduction scheme based on a secondary domain intercorrelation enhanced network. Specifically, we introduced a compressed Poisson noise corruption model and combined the secondary domain intercorrelation prior with a deep neural network especially designed for signal-dependent compression noise reduction. Experimental results showed that the proposed network is superior to the existing state-of-the-art restoration alternatives on classical images, the LIVE1 dataset, and the SIDD dataset.
KSP 제안 키워드
Art restoration, Camera sensor, Challenging issues, Coding system, Deep neural network(DNN), Image coding, Noise reduction(NR), Poisson Noise, Signal-dependent noise, block-based coding, compressed images
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