http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Huanlin Liu (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Huanlin Liu,Hongyue Dai,Yong Chen,Peijie Xia 한국광학회 2016 Current Optics and Photonics Vol.20 No.1
Visible Light Communication (VLC) using Light Emitting Diodes (LEDs) within the existing lightinginfrastructure can reduce the implementation cost and may gain higher throughput than radio frequency(RF) or Infrared (IR) based wireless systems. Current indoor VLC systems may suffer from poor downlinkresource allocation problems and small system throughput. To address these two issues, we propose analgorithm called a conflict graph scheduling (CGS) algorithm, including a conflict graph and a schemethat is based on the conflict graph. The conflict graph can ensure that users are able to transmit datawithout interference. The scheme considers the user fairness and system throughput, so that they both canget optimum values. Simulation results show that the proposed algorithm can guarantee significantimprovement of system throughput under the premise of fairness.
-
GAN-Based Local Lightness-Aware Enhancement Network for Underexposed Images
Yong Chen,Meiyong Huang,Huanlin Liu,Jinliang Zhang,Kaixin Shao 한국정보처리학회 2022 Journal of information processing systems Vol.18 No.4
Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficientconsideration during enhancement procedure will result in over-/under-exposure, loss of details and colordistortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed inthis paper based on the guidance of the unpaired low-/normal-light images. The key components in our networkinclude super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), anddenoising-scaling module (DSM). The SRM improves the resolution of the low-light input images beforeillumination enhancement. Such design philosophy improves the effectiveness of texture details preservationby operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectivelydistinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistencyof illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, localregion discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with humanaesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Bothqualitative and quantitative experiments demonstrate that our approach achieves favorable results, whichindicates its superior capacity on illumination and texture details restoration.
DBpia
ScienceON연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
