scholarly journals Transmission Enhancement in Coaxial Hole Array Based Plasmonic Color Filter for Image Sensor Applications

2018 ◽  
Vol 10 (4) ◽  
pp. 1-9 ◽  
Author(s):  
Xin He ◽  
Nicholas O'Keefe ◽  
Yajing Liu ◽  
Dechuan Sun ◽  
Hemayet Uddin ◽  
...  
2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR


2021 ◽  
Vol 29 (5) ◽  
pp. 7767
Author(s):  
Ping Zhang ◽  
Deqiang Zhao ◽  
Xiaosong Wang ◽  
Shaomeng Wang ◽  
Yusuke Sakai ◽  
...  

2019 ◽  
Vol 63 (6) ◽  
pp. 60410-1-60410-12
Author(s):  
Irina Kim ◽  
Seongwook Song ◽  
Soonkeun Chang ◽  
Sukhwan Lim ◽  
Kai Guo

Abstract Latest trend in image sensor technology allowing submicron pixel size for high-end mobile devices comes at very high image resolutions and with irregularly sampled Quad Bayer color filter array (CFA). Sustaining image quality becomes a challenge for the image signal processor (ISP), namely for demosaicing. Inspired by the success of deep learning approach to standard Bayer demosaicing, we aim to investigate how artifacts-prone Quad Bayer array can benefit from it. We found that deeper networks are capable to improve image quality and reduce artifacts; however, deeper networks can be hardly deployed on mobile devices given very high image resolutions: 24MP, 36MP, 48MP. In this article, we propose an efficient end-to-end solution to bridge this gap—a duplex pyramid network (DPN). Deep hierarchical structure, residual learning, and linear feature map depth growth allow very large receptive field, yielding better details restoration and artifacts reduction, while staying computationally efficient. Experiments show that the proposed network outperforms state of the art for standard and Quad Bayer demosaicing. For the challenging Quad Bayer CFA, the proposed method reduces visual artifacts better than state-of-the-art deep networks including artifacts existing in conventional commercial solutions. While superior in image quality, it is 2‐25 times faster than state-of-the-art deep neural networks and therefore feasible for deployment on mobile devices, paving the way for a new era of on-device deep ISPs.


Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4697 ◽  
Author(s):  
Yeahwon Kim ◽  
Hohyung Ryu ◽  
Sunmi Lee ◽  
Yeon Ju Lee

Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm.


2012 ◽  
Vol 182-183 ◽  
pp. 929-932
Author(s):  
Jie Yin ◽  
Shu Yang ◽  
Chong Pan

Numerical simulation about transmission enhancement phenomenon on metal film hole array has been finished in the paper by East FDTD commercial software. In some wavelengths, relative transmission rate is more than 1 and we also study that the thickness of metal plate, the size of the hole and period on influence of the transmission rate. Transmission enhancement peak lowers with the increasing of silver film thickness, enlarger along with the increasing of the aperture, And when period of hole gets larger, transmission peak will shift.


2007 ◽  
Vol 274 (1) ◽  
pp. 236-240 ◽  
Author(s):  
Yuegang Chen ◽  
Yanhua Wang ◽  
Yan Zhang ◽  
Shutian Liu

Author(s):  
Jin-Yi Lin ◽  
Kwuang-Han Chang ◽  
Chen-Che Kao ◽  
Shih-Chin Lo ◽  
Yan-Jiun Chen ◽  
...  

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