脱模
人工智能
计算机科学
计算机视觉
彩色滤光片阵列
GSM演进的增强数据速率
图像复原
图像传感器
深度学习
图像质量
滤波器(信号处理)
点(几何)
迭代重建
拜尔滤镜
图像(数学)
彩色凝胶
图像处理
彩色图像
数学
图层(电子)
薄膜晶体管
几何学
有机化学
化学
作者
Irina Kim,Dongpan Lim,Young–Il Seo,Jeongguk Lee,Young-Dae Choi,Seongwook Song
出处
期刊:2021 IEEE Region 10 Symposium (TENSYMP)
日期:2021-08-23
被引量:1
标识
DOI:10.1109/tensymp52854.2021.9550945
摘要
There is a recent trend change in using non-Bayer Color Filter Array (CFA) patterns, allowing superior image quality in low-light conditions. However, these new CFA patterns have weak point on full image reconstruction showing severe visual artifacts and low details reconstruction after demosaicing. In this work, we address aforementioned problems by using deep learning approach for new CFA type - Nonacell or Nonapixel, introduced by Samsung 108MP HMX CMOS image sensor. Experimental results show that proposed method not only allows suppression of visual artifacts and perfect details and edge restoration, but also shows superior objective image quality, exceeding 40dB in CPSNR for popular Kodak dataset. Finally, our method is computationally efficient due to network structure and feasible for on-device deployment after optimization.
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